Methods for improving the performance of an analyte monitoring system

ABSTRACT

The present invention relates to methods to increase the number of analyte-related signals used to provide analyte measurement values, e.g., when two or more analyte-related signals are used to obtain a single analyte measurement value a “rolling” value based on the two or more signals can be employed. In another aspect, interpolation and/or extrapolation methods are used to estimate unusable, missing or error-associated analyte-related signals. Further, interpolation and extrapolation of values are employed in another aspect of the invention that reduces the incident of failed calibrations. Further, the invention relates to methods, which employ gradients and/or predictive algorithms, to provide an alert related to analyte values exceeding predetermined thresholds. The invention includes the above-described methods, one or more microprocessors programmed to execute the methods, one or more microprocessors programmed to execute the methods and control at least one sensing and/or sampling device, and monitoring systems employing the methods described herein.

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application is related to U.S. Provisional PatentApplications Serial Nos. 60/300,511, filed Jun. 22, 2001, and60/342,297, filed Dec. 20, 2001, from which priority is claimed under 35USC § 119(e)(1), and which applications are incorporated herein byreference in their entireties.

TECHNICAL FIELD

[0002] The present invention includes, but is not limited to, methodsfor improving the performance of an analyte monitoring system thatprovides a series of analyte-related signals over time, one or moremicroprocessors programmed to execute the methods, one or moremicroprocessors programmed to execute the methods and control a sensingdevice, one or more microprocessors programmed to execute the methods,control a sensing device, and control a sampling device, and monitoringsystems employing the methods of the present invention. In oneembodiment, the methods relate to glucose monitoring systems. In oneaspect of the present invention, a rolling value is employed usingsignal data provided by an analyte sensor. The rolling value method ofthe present invention provides for more frequent updating and reportingof analyte measurement values. Another aspect of the present inventionis employing interpolation and/or extrapolation methods to providemissing or error-associated signals in a series of analyte-relatedsignals. Another aspect of the invention relates to methods of providingan alert related to analyte values exceeding predetermined thresholds(e.g., high and/or low thresholds) or ranges of values. In this aspectof the invention a gradient method and/or predictive algorithm methodmay be used. Yet another aspect of the present invention is a method forprocessing data from an analyte monitoring system that reduces theincidence of failed calibrations. The present invention includes, but isnot limited to, methods, microprocessors programmed to execute themethods, and monitoring systems (comprising, for example, a samplingdevice, a sensing device, and one or more microprocessors programmed tocontrol, for example, (i) a measurement cycle utilizing the sampling andsensing devices, and (ii) data gathering and data processing related tothe methods of the present invention).

BACKGROUND OF THE INVENTION

[0003] Numerous systems for monitoring analyte (e.g., glucose) amount orconcentration in a subject are known in the art, including, but notlimited to the following: U.S. Pat. Nos. 5,362,307, 5,279,543,5,695,623; 5,713,353; 5,730,714; 5,791,344; 5,840,020; 5,995,860;6,026,314; 6,044,285; 6,113,537; 6,188,648, 6,326,160, 6,309,351,6,299,578, 6,298,254, 6,284,126, 6,272,364, 6,233,471, 6,201,979,6,180,416, 6,144,869, 6,141,573, 6,139,718, 6,023,629, 5,989,409,5,954,685, 5,827,183, 5,771,890, and 5,735,273.

[0004] Self monitoring of blood glucose (BG) is a critical part ofmanaging diabetes. However, most procedures for obtaining suchinformation are invasive, painful and provide only periodicmeasurements. Results from the Diabetes Control and Complication TrialResearch Group, (The Diabetes Control and Complication Trial ResearchGroup. N Engl J Med. 1993;329:997-1036), UK Prospective Diabetes Study(UK Prospective Diabetes Study (UKPDS) Group. Lancet. 1998;352:837-853),and Kumamoto trials (Ohkubo Y, Kishikawa H, Araki E, et al. Diabetes ResClin Pract. 1995;28: 103-117) showed that a tight glucose controlregiment, which uses frequent glucose measurements to guide theadministration of insulin or oral hypoglycemic agents, leads to asubstantial decrease in the long-term complications of diabetes;however, there was a 3-fold increase in hypoglycemic events (TheDiabetes Control and Complication Trial Research Group. N Engl J Med.1993;329:997-1036.). Moreover, as many as 7 BG measurements per day werenot sufficient to detect a number of severe hypoglycemic andhypoglycemic events (Ohkubo Y, Kishikawa H, Araki E, et al. Diabetes ResClin Pract. 1995;28:103-117.).

[0005] The GlucoWatch® (Cygnus, Inc., Redwood City, Calif.) biographerprovides a means to obtain painless, automatic, frequent and noninvasiveglucose measurements (see, for example, U.S. Pat. Nos. 6,326,160,6,309,351, 6,299,578, 6,298,254, 6,284,126, 6,272,364, 6,233,471,6,201,979, 6,180,416, 6,144,869, 6,141,573, 6,139,718, 6,023,629,5,989,409, 5,954,685, 5,827,183, 5,771,890, and 5,735,273). The deviceprovides up to 3 readings per hour for as long as 12 hours after asingle BG measurement for calibration (Tamada, et al., JAMA282:1839-1844, 1999).

[0006] Such a monitoring system, which gives automatic and frequentmeasurement, supplies detailed information on glucose patterns andtrends that might identify opportunities for improved BG control.Automatic readings also provide the opportunity for an alarm to besounded in response to values below a user-selected alert level or as aresult of rapid declines in the measured glucose values. Such alarmsprovide a method to reduce the risks of hypoglycemia and make intensivetherapy for persons with diabetes safer and acceptable to more patients.

[0007] Further, such monitoring systems can be used to measure an amountor concentration, in a subject, of one or more analytes, where the oneor more analytes may be in addition to or other than glucose (see, e.g.,WO 96/00109, published Jan. 4, 1996.

[0008] The present invention offers methods of improving performance ofanalyte monitoring systems that supply a series of analyte-relatedsignals over time, for example, the GlucoWatch biographer.

SUMMARY OF THE INVENTION

[0009] The present invention includes, but is not limited to, methodsfor improving the performance of an analyte monitoring system thatprovides a series of analyte-related signals over time, one or moremicroprocessors programmed to execute the methods, one or moremicroprocessors programmed to execute the methods and control a sensingdevice, one or more microprocessors programmed to execute the methods,control a sensing device, and control a sampling device, and monitoringsystems employing the methods (comprising, for example, a samplingdevice, a sensing device, and one or more microprocessors programmed tocontrol, for example, (i) a measurement cycle utilizing the sampling andsensing devices, and (ii) data gathering and data processing related tothe methods of the present invention).

[0010] In a first aspect, the present invention relates to methods forcalculating a series of average signals wherein (i) each average signalis calculated based on two or more contiguous (i.e., next to or near intime or sequence) signals in the series, and (ii) each average signalprovides a measurement related to the amount or concentration of analytein the subject; or, alternately calculating a series of sums, wherein(i) each summed signal is calculated based on two or more contiguous(i.e., next to or near in time or sequence) signals in the series, and(ii) each summed signal provides a measurement related to the amount orconcentration of analyte in the subject. In this aspect the inventionalso comprises one or more microprocessors programmed to execute themethods, and monitoring systems employing the methods.

[0011] This aspect of the present invention is used, for example, in amethod for monitoring an amount or concentration of analyte present in asubject, said method comprising:

[0012] providing a series of signals over time wherein each signal isrelated to the analyte amount or concentration in the subject; and

[0013] calculating a series of average signals wherein (i) each averagesignal is calculated based on two or more contiguous (i.e., next to ornear in time or sequence) signals in the series, and (ii) each averagesignal provides a measurement related to the amount or concentration ofanalyte in the subject; or calculating a series of sums, wherein (i)each summed signal is calculated based on two or more contiguous (i.e.,next to or near in time or sequence) signals in the series, and (ii)each summed signal provides a measurement related to the amount orconcentration of analyte in the subject. Missing signals in the seriesmay be estimated using interpolation and/or extrapolation, and suchestimated signals can be used in said calculations.

[0014] In this aspect, the present invention relates to methods ofincreasing the number of analyte measurement values related to theamount or concentration of an analyte in a subject as measured using ananalyte monitoring device. In this method a series of analyte-relatedsignals is obtained from the analyte monitoring device over time.Typically, two or more contiguous analyte-related signals are used toobtain a single analyte measurement value (M). In this method, pairedanalyte-related signals are typically used to calculate the measurementvalue. One improvement provided by the present method is that, prior tothe present method, such an analyte monitoring device typically usedpaired signals to obtain a single measurement value; but ananalyte-related signal from the monitoring device was not typically usedto calculate more than one analyte measurement value. In the presentmethod, the two or more contiguous analyte-related signals, used toobtain the single analyte measurement value, comprise first and lastanalyte-related signals of the series.

[0015] The method involves mathematically computing rolling analytemeasurement values, wherein (i) each rolling analyte measurement valueis calculated based on two or more contiguous analyte-related signalsfrom the series of analyte-related signals obtained from the analytemonitoring device. Subsequent rolling analyte measurement values aremathematically computed by dropping the first analyte-related signalfrom the previous rolling analyte measurement value and including ananalyte-related signal contiguous and subsequent to the lastanalyte-related signal used to calculate the previous rolling analytemeasurement value. Further rolling analyte measurement values areobtained by repeating the dropping of the first analyte-related signalused to calculate the previous rolling analyte measurement and includingan analyte-related signal contiguous and subsequent to the lastanalyte-related signal used to calculate the previous rolling analytemeasurement. Each rolling analyte measurement value provides ameasurement related to the amount or concentration of analyte in thesubject. By employing this method the number of analyte measurementvalues, derived from the analyte-related signals in the series ofanalyte-related signals obtained from the analyte monitoring device, isincreased by serially calculating rolling analyte measurement values.

[0016] In one embodiment of this aspect of the invention, the rollinganalyte measurement value is, for example, an average of two or moreanalyte-related signals; alternately, the rolling analyte measurementvalue is a sum of two or more analyte-related signals. In anotherembodiment, each analyte-related signal is represented by an integralover time, and the rolling analyte measurement value is obtained byintegral splitting.

[0017] The above method may be practiced, for example, using amonitoring device comprising a sampling device and a sensing device,wherein the series of analyte-related signals obtained from an analytemonitoring device is obtained as follows. Samples are extracted from thesubject alternately into a first collection reservoir and then into asecond collection reservoir using the sampling device, wherein (i) eachsample comprises the analyte, and (ii) the sampling device comprises thefirst and second collection reservoirs. The analyte is sensed in eachextracted sample to obtain a signal from each sample that is related tothe analyte amount or concentration in the subject, thus providing aseries of analyte-related signals. The sensing device may, for example,comprise first and second sensors, wherein the first sensor is inoperative contact with the first collection reservoir and the sensingprovides signal S^(A) _(J) (where S^(A) is the signal from sensor A, jis the time interval), the second sensor is in operative contact withthe second collection reservoir and the sensing provides signal S^(B)_(j+1) (where S^(B) is the signal from sensor B, j+1 is the timeinterval), and an analyte measurement value is obtained usinganalyte-related signal from sensor A and sensor B. In this situation,the series of rolling analyte measurement values may be calculatedemploying the following equations:

(average signal)_(J)=(S ^(B) _(j−1) +S ^(A) _(j))/2,   Eqn. 1

(average signal)_(J+1)=(S ^(A) _(J) +S ^(B) _(J+1))/2;   Eqn. 2

(average signal)_(j+2)=(S ^(B) _(j+1) +S ^(A) _(j+2))/2; etc.,   Eqn. 3

[0018] wherein (i) (j−1) is the measurement half-cycle previous to j,and (j+2) is two measurement half-cycles after j, and (ii) each averagesignal corresponds to a rolling analyte measurement value.

[0019] Alternately, the series of rolling analyte measurement values maybe calculated using the following equations:

(summed signal)_(j)=(S ^(B) _(J−1) +S ^(A) _(j));   Eqn. 4

(summed signal)_(j+1)=(S ^(A) _(J) +S ^(B) _(j+1)); and   Eqn. 5

(summed signal)_(J+2)=(S ^(B) _(J+1) +S ^(A) _(J+2)); etc.   Eqn. 6

[0020] where (j−1) is the measurement half-cycle previous to j, and(j+2) is two measurement half-cycles after j; and (ii) each summedsignal corresponds to a rolling analyte measurement value.

[0021] In one embodiment of this method, a missing or error-associatedsignal in the series of analyte-related signals obtained from theanalyte monitoring device is estimated using interpolation beforemathematically computing rolling analyte measurement values. Suchmissing or error-associated signals may also be estimated usingextrapolation before mathematically computing rolling analytemeasurement values.

[0022] In a preferred embodiment, the analyte is glucose. In oneembodiment, the analyte monitoring device comprises (i) an iontophoreticsampling device, and (ii) an electrochemical sensing device. Theanalyte-related signal may, for example, be a current or a chargerelated to analyte amount or concentration of analyte in the subject.

[0023] One or more microprocessors may be utilized to mathematicallycompute rolling analyte measurement values employing the methodsdescribed herein. Further, such one or more microprocessors may be usedto control operation of the components of the analyte-monitoring system(e.g., a sampling device and a sensing device of the monitoring system).In addition, the one or more microprocessors may control operation ofother components, further algorithms, calculations, and/or the providingof alerts to a subject (user of the analyte-monitoring system). In thisembodiment of the present invention one or more microprocessors may beutilized to execute the method as well as to control components of ananalyte-monitoring system, for example, control obtaining samples andsensing analyte concentration in each obtained sample to provide aseries of signals.

[0024] The present invention also includes analyte-monitoring devicesemploying the above methods.

[0025] In a second aspect the present invention comprises methods ofinterpolation and/or extrapolation to provide missing signals, where aseries of signals is provided by an analyte monitoring system. Such ananalyte monitoring system may comprise one or more sensors that providethe series of signals.

[0026] In one embodiment, the present invention includes the use ofrelationships between the signals obtained from different sensors toperform interpolation and/or extrapolation of estimated values. Forexample, in a two sensor system a ratio of signals obtained from a firstsensor relative to a second sensor may be employed in suchinterpolations and/or extrapolations to estimate signal values.

[0027] One embodiment of this second aspect of the present inventionincludes a method of replacing unusable analyte-related signals whenemploying an analyte monitoring device to measure an analyte amount orconcentration in a subject. A series of analyte-related signals,obtained from the analyte monitoring device over time, is providedwherein each analyte-related signal is related to the amount orconcentration of analyte in the subject. An unusable analyte-relatedsignal is replaced with an estimated signal, for example, by either:

[0028] (A) if one or more analyte-related signals previous to theunusable analyte-related signal and one or more analyte-related signalssubsequent to the unusable analyte related signal are available, theninterpolation is used to estimate the unusable, interveninganalyte-related signal; or

[0029] (B) if two or more analyte-related signals previous to theunusable analyte-related signal are available, then extrapolation isused to estimate the unusable, subsequent analyte-related signal.

[0030] In this method, the analyte monitoring device may comprise one ormore sensor devices and a relationship between the signals obtained fromthe different sensor devices is used in interpolation and/orextrapolation calculation of estimated values. In one embodiment, thesensor device comprises two sensor elements and a ratio of signalsobtained from a first sensor relative to a second sensor is employed ininterpolation and/or extrapolation calculation of estimated signalvalues. For example, the analyte monitoring device may comprises asampling device and a sensing device, wherein providing the series ofanalyte-related signals obtained from an analyte monitoring devicecomprises:

[0031] extracting a sample from the subject alternately into a firstcollection reservoir and then into a second collection reservoir usingthe sampling device, wherein (i) each sample comprises the analyte, and(ii) the sampling device comprises the first and second collectionreservoirs; and

[0032] sensing the analyte in each extracted sample to obtain a signalfrom each sample that is related to the analyte amount or concentrationin the subject, thus providing a series of analyte-related signals, thesensing device comprising first and second sensors, wherein the firstsensor is in operative contact with the first collection reservoir andthe sensing provides signal S^(A) _(J) (where S^(A) is the signal fromsensor A, j is the time interval), the second sensor is in operativecontact with the second collection reservoir and the sensing providessignal S^(B) _(j+1) (where S^(B) is the signal from sensor B, j+1 is thetime interval), and an analyte measurement value is obtained usinganalyte-related signal from sensor A and sensor B.

[0033] A relationship between the signals obtained from differentsensors may be used in interpolation and/or extrapolation calculation ofestimated values. For example, the relationship between the signals fromthe different sensors may take the form of a smoothed ratio:

R _(l) ^(s) =wR _(l)+(1−W)R_(l−1) ^(s)   Eqn. 10

[0034] wherein, for example, R_(i) is the A/B or B/A signal ratio for ai^(th) measurement cycle, R^(S) _(i) is smoothed R for a i^(th)measurement cycle, and w is a smoothing factor and is represented by afraction between and inclusive of 0 through 1, and R^(S) _(i−1) is asmoothed ratio for the (i−1)^(th) measurement cycle, wherein the i^(th)measurement cycle is composed of first and second half-cycles and thesecond half-cycle value of the i^(th) measurement cycle precedes S_(j).In one embodiment, , wherein a smoothed A/B ratio and a smoothed B/Aratio are employed, and the ratios are as follows: $\begin{matrix}{\left( \frac{A}{B} \right)_{s,i} = {{w\left( \frac{A}{B} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{A}{B} \right)_{s,{i - 1}}}}} & {{Eqn}.\quad \text{9A}} \\{\left( \frac{B}{A} \right)_{s,i} = {{w\left( \frac{B}{A} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{B}{A} \right)_{s,{i - 1}}}}} & {{Eqn}.\quad \text{9B}}\end{matrix}$

[0035] wherein (A/B)_(S,1) and (B/A)_(S,1) refer to “smoothed” AB ratiosfor measurement cycle i, (A/B)_(i) and (B/A)_(i), refer to the AB ratiofor measurement cycle i, and (A/B)_(s,l−1) and (B/A)_(S,l−1) refer tothe smoothed AB ratio from the previous measurement cycle i−1.

[0036] For interpolation in the situation where both S_(J) and S_(j+2)are signals from the B sensor (S^(B) _(J) and S^(B) _(J+2)), and S_(J+1)is being estimated for the A sensor signal (S^(AE) _(j+1)),interpolation Eqn. 7A may be employed as follows: $\begin{matrix}{S_{j + 1}^{AE} = {\frac{A}{B}\left\{ {S_{j}^{B} + {\left( {S_{j + 2}^{B} - S_{j}^{B}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\quad \text{7A}}\end{matrix}$

[0037] wherein t_(j) is a measurement half-cycle, t_(j+1), onesubsequent half-cycle, and t_(j+2) two subsequent half-cycles.

[0038] For interpolation in the situation where both S_(J) and S_(J+2)are signals from the A sensor (S^(A) _(J) and S^(A) _(J+2)), and S_(J+1)is being estimated for the B sensor signal (S^(BE) _(j+1)),interpolation Eqn. 7C may be employed as follows: $\begin{matrix}{S_{j + 1}^{BE} = {\frac{B}{A}\left\{ {S_{j}^{A} + {\left( {S_{j + 2}^{A} - S_{j}^{A}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\quad \text{7C}}\end{matrix}$

[0039] wherein t_(J), is a measurement half-cycle, t_(J+1), onesubsequent half-cycle, and t_(j+2) two subsequent half-cycles.

[0040] For extrapolation in the situation where S_(j) is signal fromsensor A (S^(A) _(J)) and S_(J+1) is signal from B sensor (S^(B)_(j+1)), and S_(j+2) is being estimated for the A sensor signal (S^(AE)_(j+2)), extrapolation Eqn. 8A may be employed as follows:$\begin{matrix}{S_{j + 2}^{AE} = {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} + \left\lbrack {\left\{ {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} - S_{j}^{A}} \right\} \frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\quad \text{8A}}\end{matrix}$

[0041] wherein t_(J) is a measurement half-cycle, t_(j+1), onesubsequent half-cycle, and t_(j+2) two subsequent half-cycles.

[0042] For extrapolation in the situation where S_(J) is signal from theB sensor (S^(B) _(j)) and S_(J+1) is signal from the A sensor (S^(A)_(J+1)), and S_(j+2) is being estimated for the B sensor signal (S^(BE)_(J+2)), extrapolation Eqn. 8C may be employed as follows:$\begin{matrix}{S_{j + 2}^{BE} = {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} + \left\lbrack {\left\{ {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} - S_{j}^{B}} \right\} \frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\quad \text{8C}}\end{matrix}$

[0043] wherein t_(J) is a measurement half-cycle, t_(J+1), onesubsequent half-cycle, and t_(j+2) two subsequent half-cycles.

[0044] In one embodiment the analyte is glucose. The analyte monitoringdevice may, for example, comprise (i) an iontophoretic sampling device,and (ii) an electrochemical sensing device. The analyte-related signalmay be, e.g., a current or a charge related to analyte amount orconcentration of analyte in the subject.

[0045] One or more microprocessors may be utilized to mathematicallycompute estimated signals employing the methods described herein.Further, such one or more microprocessors may be used to controloperation of the components of the analyte monitoring system (e.g., asampling device and a sensing device of the monitoring system). Inaddition, the one or more microprocessors may control operation of othercomponents, further algorithms, calculations, and/or the providing ofalerts to a subject (user of the analyte monitoring system).

[0046] The present invention also includes analyte monitoring devicesemploying the above methods.

[0047] In a third aspect, the present invention relates to a method forreducing the incidence of failed calibration for an analyte monitoringsystem that is used to monitor an amount or concentration of analytepresent in a subject, where the monitoring system provides a series ofsignals or measurement values. For example, in one embodiment, themethod comprises:

[0048] extracting a series of samples from the subject using a samplingdevice, said extracting alternately into a first collection reservoirand then into a second collection reservoir, wherein (1) each samplecomprises the analyte, and (2) said sampling device comprises said firstand second collection reservoirs;

[0049] sensing the analyte in each extracted sample to obtain a signalfrom each sample that is related to the analyte amount or concentrationin the subject, thus providing a series of signals, said sensing devicecomprising a first sensor (A) and second sensor (B), wherein (1) saidfirst sensor (A) is in operative contact with said first collectionreservoir and said second sensor (B) is in operative contact with saidsecond collection reservoir, (2) two consecutive signals comprise ameasurement cycle, and each of the two consecutive signals is half-cyclesignal; and

[0050] performing a calibration method to relate analyte amount orconcentration in the subject to signals obtained from the sensors, saidcalibration method comprising:

[0051] (i) obtaining a first half-cycle signal S_(j), where a half-cyclesignal S_(J+1), or an estimate thereof, and a half-cycle signal S_(j+2),or an estimate thereof, are both used in the calibration method so thatthe sensor signals correlate to the analyte amount or concentration inthe subject, wherein the calibration method also employs an analytecalibration value that is independently determined;

[0052] (ii) providing the analyte calibration value;

[0053] (iii) selecting a conditional statement selected from the groupconsisting of:

[0054] (a) if neither the second half-cycle signal S_(j+1) nor the thirdhalf-cycle signal S_(J+2) comprise errors, then S_(j+1) and S_(J+2) areused in the calibration method;

[0055] (b) if only the second half-cycle signal S_(j+1) comprises anerror, then an estimated signal S^(E) _(j+1) is obtained by determiningan interpolated value using signal S_(J) and S_(j+2), wherein saidinterpolated value is S^(E) _(j+1), and S^(E) _(j+1) and S_(J+2) areused in the calibration method;

[0056] (c) if only the third half-cycle signal S_(J+2) comprises anerror, then an estimated signal S^(E) _(J+2) is obtained by determiningan extrapolated value using signal S_(j) and S_(J+1), wherein saidextrapolated value is S^(E) _(j+2), and S_(j+1) and SE +₂ are used inthe calibration method; and

[0057] (d) if both the second half-cycle signal S_(J+1) and the thirdhalf-cycle signal S_(J+2) comprise errors, then return to (i) to obtaina new half-cycle signal S_(j) from a later measurement half-cycle thanthe first half-cycle signal,

[0058] wherein said calibration method reduces the incidence of failedcalibration for the analyte monitoring system.

[0059] In the above-described method for reducing the incidence offailed calibration, before performing said calibration method, a ratioof the signals obtained from the first sensor (A) and the second sensor(B) may be determined based on a series of signals obtained from firstsensor (A) and second sensor (B), said ratio representing therelationship between sensor signals. One or more microprocessors may beprogrammed to provide the ratio.

[0060] The ratio of signals can be a smoothed ratio of the form:

R ₁ ^(s) =wR _(i)+(1−w)R _(i−1) ^(s)   Eqn. 10

[0061] wherein, R_(i) is the A/B or B/A ratio for a i^(th) measurementcycle, R^(S) ₁, is smoothed R for a i^(th) measurement cycle, and w is asmoothing factor and represents a numerical, percentage value betweenand inclusive of 0 through 100%, where w is represented by a fractionbetween and inclusive of 0 through 1, and R^(S) _(i−1) is a smoothedratio for the (i−1)^(th) measurement cycle, wherein the i^(th)measurement cycle is composed of first and second half-cycles and thesecond half-cycle value of the i^(th) measurement cycle precedes S_(j).A single R^(S) ₁, may be used or more than one such ratio may beemployed.

[0062] In one embodiment of the method for reducing the incidence offailed calibration in a two sensor system, two smoothed AB ratios may beemployed: $\begin{matrix}{\left( \frac{A}{B} \right)_{s,i} = {{w\left( \frac{A}{B} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{A}{B} \right)_{s,{i - 1}}}}} & {{Eqn}.\quad \text{9A}} \\{\left( \frac{B}{A} \right)_{s,i} = {{w\left( \frac{B}{A} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{B}{A} \right)_{s,{i - 1}}}}} & {{Eqn}.\quad \text{9B}}\end{matrix}$

[0063] In Eqn. 9A and Eqn. 9B, (A/B)_(s,i) and (B/A)_(s,i) refer to“smoothed” AB ratios for measurement cycle i, (A/B)_(i) and (B/A)_(i),refer to the AB ratio for measurement cycle i, and (A/B)_(s,l−1) and(B/A)_(s,l−1), refer to the smoothed AB ratio from the previousmeasurement cycle i−1. In the Holt-Winters smoothing presented above,the determination of the smoothed AB ratio depends on the adjustableparameter w (a weighting factor). In one embodiment of the presentinvention, w is 70% (0.70).

[0064] In one embodiment of the method for reducing the incidence offailed calibration in a two-sensor system (wherein two AB ratios areemployed, conditional statement (b) is selected, and said interpolatedvalue is determined by an interpolation calculation) Eqn. 7A throughEqn. 7D may be employed for interpolation in the following situations:

[0065] in the situation where both S_(j) and S_(j+2) are signals fromthe B sensor (S^(B) _(j) and S^(B) _(j+2)), and S_(J+1) is beingestimated for the A sensor signal (S^(AE) _(j+1)), interpolation Eqn. 7Amay be employed as follows: $\begin{matrix}{S_{j + 1}^{AE} = {\frac{A}{B}\left\{ {S_{j}^{B} + {\left( {S_{j + 2}^{B} - S_{j}^{B}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\quad \text{7A}}\end{matrix}$

[0066] wherein t is the time interval, for example, measurementhalf-cycle t_(j), one subsequent half-cycle, t_(j+1), or two subsequenthalf-cycles t_(j+2). When the points are equally spaced, that is when2(t_(j+1)-t_(j))=(t_(j+2)−t_(J)), then Eqn. 7A reduces to the followingEqn. 7B: $\begin{matrix}{S_{j + 1}^{AE} = {\frac{A}{B}\left( \frac{S_{j}^{B} + S_{j + 2}^{B}}{2} \right)}} & {{Eqn}.\quad \text{7B}}\end{matrix}$

[0067] In the situation where both S_(J) and S_(J+2) are signals fromthe A sensor (S^(A) _(J) and S^(A) _(j+2)), and S_(j+1) is beingestimated for the B sensor signal (S^(BE) _(j+1)), interpolation Eqn. 7Cmay be employed as follows: $\begin{matrix}{S_{j + 1}^{BE} = {\frac{B}{A}\left\{ {S_{j}^{A} + {\left( {S_{j + 2}^{A} - S_{j}^{A}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\quad \text{7C}}\end{matrix}$

[0068] When the points are equally spaced, that is when2(t_(j+1)-t_(j))=(t_(j+2)-t_(j)), then Eqn. 7C reduces to the followingEqn. 7D: $\begin{matrix}{S_{j + 1}^{BE} = {\frac{B}{A}{\left( \frac{S_{j}^{A} + S_{j + 2}^{A}}{2} \right).}}} & {{Eqn}.\quad \text{7D}}\end{matrix}$

[0069] In a further embodiment of the method for reducing the incidenceof failed calibration in a two sensor system (wherein two AB ratios areemployed, conditional statement (c) is selected, and said extrapolatedvalue is determined by an extrapolation method) Equations 2A and 2B maybe employed for extrapolation in the following situations:

[0070] in the situation where S_(J) is signal from sensor A (S^(A) _(j))and S_(j+1) is signal from B sensor (S^(B) _(j+1)), and S_(j+2) is beingestimated for the A sensor signal (S^(AE) _(J+2)), extrapolation Eqn. 8Amay be employed as follows: $\begin{matrix}{S_{j + 2}^{AE} = {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} + \left\lbrack {\left\{ {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} - S_{j}^{A}} \right\} \frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\quad \text{8A}}\end{matrix}$

[0071] When the points are equally spaced, that is when(t_(J+2)−t_(j+1))=(t_(j+1)−t_(j)), then Eqn. 8A reduces to the followingEqn. 8B: $\begin{matrix}{S_{j + 2}^{AE} = {{2\frac{A}{B}S_{j + 1}^{B}} - S_{j}^{A}}} & {{Eqn}.\quad \text{8B}}\end{matrix}$

[0072] In the situation where S_(j) is signal from the B sensor (S^(B)_(j)) and S_(j+1) is signal from the A sensor (S^(A) _(j+1)), andS_(j+2) is being estimated for the B sensor signal (S^(BE) _(J+2)),extrapolation Eqn. 8C may be employed as follows: $\begin{matrix}{S_{j + 2}^{BE} = {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} + \left\lbrack {\left\{ {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} - S_{j}^{B}} \right\} \frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\quad \text{8C}}\end{matrix}$

[0073] When the points are equally spaced, that is when(t_(J+2)−t_(J+1))=(t_(J+1)−t_(J)), then Eqn. 8C reduces to the followingEqn. 8D: $\begin{matrix}{S_{j + 2}^{BE} = {{2\frac{B}{A}S_{j + 1}^{A}} - {S_{j}^{B}.}}} & {{Eqn}.\quad \text{8D}}\end{matrix}$

[0074] In these embodiments of the present invention one or moremicroprocessors may be utilized to execute the interpolation methods,extrapolation methods, and/or the method for reducing the incidence offailed calibration, as well as to control components of an analytemonitoring system, for example, control extracting samples, sensinganalyte concentration in each obtained sample, and selecting conditionalstatements based on obtained criteria.

[0075] In this third aspect of the invention, the method may furthercomprise waiting for an un-skipped half-cycle signal (S_(J)) beforeinitiating the calibration method.

[0076] In one embodiment of this third aspect of the present invention,the analyte is glucose. The analyte monitoring device may, for example,comprise (i) an iontophoretic sampling device, and (ii) anelectrochemical sensing device. The analyte-related signal may be, e.g.,a current or a charge related to analyte amount or concentration ofanalyte in the subject.

[0077] One or more microprocessors may be utilized to control operationof the calibration method employing the methods described herein.Further, such one or more microprocessors may be used to controloperation of the components of the analyte monitoring system (e.g., asampling device and a sensing device of the monitoring system). Inaddition, the one or more microprocessors may control operation of othercomponents, further algorithms, calculations, and/or the providing ofalerts to a subject (user of the analyte monitoring system).

[0078] The present invention also includes analyte monitoring devicesemploying the above methods.

[0079] In a fourth aspect, the present invention teaches a methodcomprising waiting for an unskipped (i.e., error free or good signal)half-cycle signal before initiating a calibration sequence (e.g., beforeopening a calibration window inviting the user to provide to amonitoring system an independently determined analyte calibrationvalue).

[0080] In a fifth aspect, the present invention describes methods forpredicting an analyte concentration-related event when an analyte levelfalls above or below predetermined thresholds or outside of apredetermined range of reference values, microprocessors programmed toexecute these methods, and analyte monitoring systems employing thesemethods. The methods provide for predicting an analyteconcentration-related event in a subject being monitored for levels of aselected analyte. The methods of the invention typically employ multipleparameters to be used in prediction of the hypoglycemic event. Suchparameters include, but are not limited to, current glucose readings(reflecting glucose amount or concentration in the subject), one or morepredicted future glucose reading, time intervals, trends, skinconductance, and skin temperature. In one aspect, the analyte beingmonitored is glucose and the present invention comprises a method forpredicting a hypoglycemic and/or hyperglycemic event in a subject.

[0081] The method comprises determining threshold values (or ranges ofvalues) for the selected parameters, wherein the threshold values (orranges of values) are indicative of an analyte concentration-relatedevent in the subject: e.g., determining a threshold glucose value (orrange of values) that corresponds to a hypoglycemic event. A series ofanalyte measurement values is typically obtained at selected timeintervals. In one embodiment the time intervals are evenly spaced. Sucha series may be obtained, for example, using a method comprising:extracting a sample comprising the analyte, e.g., glucose, from thesubject using a transdermal sampling system that is in operative contactwith a skin or mucosal surface of the subject; obtaining a raw signalfrom the extracted analyte, wherein the raw signal is specificallyrelated to analyte amount or concentration in the subject; correlatingthe raw signal with an analyte measurement value indicative of theamount or concentration of analyte present in the subject at the time ofextraction; and

[0082] repeating the extracting, obtaining, and correlating to provide aseries of measurement values at selected time intervals. In oneembodiment, the sampling system used to extract samples is maintained inoperative contact with the skin or mucosal surface of the subject duringthe extracting, obtaining, and correlating to provide for frequentanalyte measurements (e.g., glucose measurements).

[0083] In this aspect of the present invention, one or more gradientmethods may be employed to examine the trend of analyte values, and/orone or more predictive algorithms may be employed to predict an analytemeasurement value for a further time interval subsequent to the seriesof measurement values. In one embodiment of this aspect of the presentinvention, the series of measurement values comprises two or morediscrete values.

[0084] Several models for the determination of a gradient (i.e., therate of change) are as follows: $\begin{matrix}{{{Model}\quad A\text{:}\quad \frac{y_{(n)} - y_{({n - 1})}}{\Delta \quad t}\quad \left( {{concentration}/{time}} \right)};{{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 1})}} \right)}} \\{{{Model}\quad B\text{:}\quad \frac{y_{(n)} - y_{({n - 1})}}{y_{({n - 1})}\Delta \quad t}\quad \left( {{fractional}\quad {{change}/{time}}} \right)};{{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 1})}} \right)}} \\{{{Model}\quad C\text{:}\quad \frac{y_{(n)} - y_{({n - 2})}}{\Delta \quad t}\quad \left( {{concentration}/{time}} \right)};{{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 2})}} \right)}} \\{{{Model}\quad D\text{:}\quad \frac{y_{(n)} - y_{({n - 2})}}{y_{({n - 2})}\Delta \quad t}\quad \left( {{fractional}\quad {{change}/{time}}} \right)};{{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 2})}} \right)}} \\{{{Model}\quad E\text{:}\quad {{Average}\quad\left\lbrack {\frac{y_{(n)} - y_{({n - 1})}}{\Delta \quad t_{1}},\frac{y_{({n - 1})} - y_{({n - 2})}}{\Delta \quad t_{2}},\frac{y_{(n)} - y_{({n - 2})}}{\Delta \quad t_{3}}} \right\rbrack}}\quad} \\{\quad {\left( {{concentration}/{time}} \right);}}\end{matrix}$

[0085] where

[0086] Δt₁=(t_((n))−t_((n−1))), Δt₂=(t_((n))−t_((n−2))), andΔt₃=(t(n)-t_((n−2))). In this model, the average is of all three valuesshown in the brackets.${{Model}\quad F\text{:}\quad \frac{y_{(n)} - y_{({n - 3})}}{y_{({n - 3})}\Delta \quad t}\quad \left( {{fractional}\quad {{change}/{time}}} \right)};{{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 3})}} \right)}$

[0087] In the above models, y_(n) stands for an analyte reading at timepoint t_((n)), y_((n−1)) an analyte reading at time point t_((n−1))(i.e., the previous reading to y_(n)), y_((n−2)) an analyte reading attime point t_((n−2)) (i.e., the reading previous to y_((n−1))),y_((n−3)) an analyte reading at time point t_((n−3)) (i.e., the readingprevious to y_((n−2))). Each of the above methods give a rate of change.Models A, C, and E give concentration change per time interval (e.g.,for glucose mg/dL/minute (milligrams of glucose per deciliter perminute) or mmol/L/minute), whereas Models B, D, and F gives fractionalchange per time interval (e.g., a percentage change in the glucosereading per minute). When using a gradient method a threshold of anacceptable rate of change is selected (for example, based onexperimental data and/or acceptable ranges of measurement values).

[0088] The selected model calculates the rate of change (e.g., in theindicated units) and an algorithm compares the calculated rate of changeto the acceptable rate of change. If the calculated rate of changesurpasses the acceptable rate of change then a alert may be provided tothe user. In one embodiment, a microprocessor employs an algorithmcomprising the selected model and calculates the rate of change (e.g.,in the indicated units). The microprocessor then employs an algorithm tocompare the calculated rate of change to a predetermined acceptable rateof change. If the calculated rate of change differs significantly fromthe acceptable rate of change then the microprocessor triggers theanalyte monitoring system to provide an alert to the user. Typicallywhen employing the gradient models, to provide a low-analyte alert(e.g., hypoglycemic event alert) the calculated rate of change isnegative and less than the predetermined threshold rate of change;and/or to provide a high-analyte alert (e.g., hyperglycemic event alert)the calculated rate of change is positive and greater than thepredetermined threshold rate of change. Alternatively, absolute valuesof the calculated and threshold rates of change may be used forcomparison. In this case, an alert is provided when the absolute valueof the calculated rate of change is greater than the absolute value ofpredetermined threshold rate of change.

[0089] Exemplary predictive algorithm methods include, but are notlimited to, the following: $\begin{matrix}{y_{({n + 1})} = {y_{(n)} + {\alpha \left( {y_{(n)} - y_{({n - 1})}} \right)} + {\frac{\alpha^{2}}{2}\left( {y_{(n)} - {2y_{({n - 1})}} + y_{({n - 2})}} \right)}}} & {{Eqn}.\quad 11} \\{y_{({n + 1})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 1})} - t_{n}} \right)}}} & {{Eqn}.\quad 12} \\{y_{({n + 1})} = {{\frac{5}{2}y_{(n)}} + {{- 2}\left( y_{({n - 1})} \right)} + {\frac{1}{2}\left( y_{({n - 2})} \right)}}} & {{Eqn}.\quad 13} \\{y_{({n + 2})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 2})}} \right)}{\left( {t_{n} - t_{({n - 2})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} & {{Eqn}.\quad 14} \\{y_{({n + 2})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} & {{Eqn}.\quad 15}\end{matrix}$

[0090] In these equations, the methods calculate the predicted value ofa variable y at time t_(n+1) (or t_(n+2), as indicated) as a function ofthat variable at the current time t_(n), as well as at a previous timeor times, e.g., t_(n−1) and/or t_(n−2)). In Eqn. 11, α is an empiricallydetermined weighting value that is typically a real number between 0and 1. Each of the above methods provides a predicted analyte value, forexample, an amount or concentration (e.g., the units may be mg/dL ormmol/L when y is a glucose reading). When using a predictive algorithm,thresholds of an acceptable range for analyte amount or concentrationare selected (for example, based on experimental data and/or acceptableranges of measurement values). High threshold values may be selected(e.g., a glucose value that is considered hyperglycemic for a subject),low threshold values may be selected (e.g., a glucose value that isconsidered hypoglycemic for a subject), and/or an acceptable range ofvalues with an associated error may also be employed.

[0091] In one embodiment, one or more microprocessors employ analgorithm comprising the selected predictive algorithm and calculatesthe predicted value (e.g., in the indicated units). The microprocessorthen employs an algorithm to compare the predicted value to thethreshold value(s). If the predicted value falls above a high threshold,below a low threshold, or outside of a predetermined range of values,then the microprocessor triggers the analyte monitoring system toprovide an alert to the user.

[0092] When the analyte being monitored is glucose and glucose readingsare provided by a glucose monitoring device y_(n) corresponds to GW_(n),a glucose value in the subject at time t_(n). Further, for prediction ofglucose values when using Eqn. 11, α is typically in the range of0.5-0.7.

[0093] In a further embodiment of this aspect of the present invention,an approach combining the above-described gradient method and predictivealgorithm method is employed. In this embodiment of the presentinvention, rate of change thresholds are determined as well as analytethresholds (or range of values). Generally, a predictive algorithm ischosen which provides a predicted analyte value at a future time point.The predicted value is compared to the threshold value for the alert. Ifthe predicted value exceeds the threshold value, then the rate of changeof the analyte is evaluated. If the rate of change of the analyte levelalso surpasses a predetermined threshold (or falls outside of a range ofvalues) then an analyte concentration-related alert is provided to thesubject in whom the analyte levels are being monitored. Of course theorder of these two comparisons (i.e., predicted value and rate ofchange) may be reversed, for example, where the rate of change isevaluated first and then the predicted value is evaluated. In oneembodiment of the present invention, a future analyte measurement value(e.g., a glucose measurement value) is predicted using Eqn. 11 and agradient analysis is performed using Model B.

[0094] When employing the above gradient methods and/or predictivealgorithms, an alert/alarm can be used to notify the subject (or user)if the predicted value is above/below a predetermined threshold.

[0095] In a further embodiment of the present invention, the rollingvalues described above are employed as the measurement data points inthe analyte concentration-related alert methods. In yet a further aspectof the present invention interpolation and/or extrapolation methods areemployed to provide missing or error-associated signals in the series ofanalyte-related signals.

[0096] One or more microprocessors may be utilized to control predictionof an analyte-concentration related event employing the methodsdescribed herein. Further, such one or more microprocessors may be usedto control operation of the components of the analyte monitoring system(e.g., a sampling device and a sensing device of the monitoring system).In addition, the one or more microprocessors may control operation ofother components, further algorithms, calculations, and/or the providingof alerts to a subject (user of the analyte monitoring system).

[0097] The present invention also includes analyte monitoring devicesemploying the above methods.

[0098] In one embodiment of this aspect of the present invention, thesample comprising glucose is extracted from the subject into acollection reservoir to obtain an amount or concentration of glucose inthe reservoir. Such one or more collection reservoirs are typically incontact with the skin or mucosal surface of the subject and the sampleis extracted using an iontophoretic current applied to the skin ormucosal surface. Further, at least one collection reservoir may comprisean enzyme that reacts with the extracted glucose to produce anelectrochemically detectable signal, e.g., glucose oxidase.Alternatively, the series of glucose measurement values may be obtainedwith a different device, for example, using a near-infraredspectrometer.

[0099] This aspect of the present invention also comprises a glucosemonitoring system useful for performing the methods of the presentinvention. In one embodiment, the glucose monitoring system comprises,in operative combination, a sensing mechanism (in operative contact withthe subject or with a glucose-containing sample extracted from thesubject, wherein the sensing mechanism obtains a raw signal specificallyrelated to glucose amount or concentration in the subject), and one ormore microprocessors in operative communication with the sensingmechanism. The microprocessors comprise programming to (i) control thesensing mechanism to obtain a series of raw signals at selected timeintervals, (ii) correlate the raw signals with measurement valuesindicative of the amount or concentration of glucose present in thesubject to obtain a series of measurement values, (iii) predict ameasurement value at a further time interval, which occurs after theseries of measurement values is obtained, (iv) compare the predictedmeasurement value to a predetermined threshold value or range of values,wherein a predicted measurement value lower than the predeterminedthreshold value is designated to be hypoglycemic, (v) calculate agradient, (vi) compare the gradient value to a predetermined thresholdvalue/trend or range of values/trends, wherein when the calculated rateof change is negative and less than the predetermined threshold rate ofchange this is indicative of a hypoglycemic event; and (vii) predict ahypoglycemic event in the subject when both (a) comparing the predictedmeasurement value to the threshold glucose value (or range of values)indicates a hypoglycemic event, and (b) comparing the gradient readingwith a threshold parameter value, range of values, or trend of parametervalues indicates a hypoglycemic event.

[0100] Embodiments of all of the above aspects of the present inventionmay include application of sampling techniques/devices including, butnot limited to, the following: iontophoresis, sonophoresis, suction,electroporation, thermal poration, laser poration, passive diffusion,microfine (miniature) lances or cannulas, biolistic, subcutaneousimplants or insertions, implanted sensing devices (e.g., in a bodycavity, blood vessel, or under a surface tissue layer) as well as laserdevices. In a preferred embodiment the method of extraction comprisesuse of a sampling device that provides transdermal extraction. In apreferred embodiment, the method of sensing the analyte, comprises useof a sensing device to obtain analyte-related signals. Examples of suchsensing devices include, but are not limited to, a biosensor or aninfrared sensor. In one embodiment of the present invention, the analytecomprises glucose. In one embodiment of the above methods, the analytecomprises glucose and said analyte monitoring system comprises atransdermal sampling device and a biosensor. In one embodiment of theabove methods, one or more microprocessors are programmed to execute themethods. Additionally, said one or more microprocessors may beprogrammed to control said sampling and sensing devices. Further, theinvention also includes an analyte monitoring system that employs anyone or more of the above-described methods, said monitoring systemcomprising one or more microprocessors programmed to control a samplingdevice, a sensing device, data acquisition, and data manipulationassociated with the methods.

[0101] These and other embodiments of the present invention will readilyoccur to those of ordinary skill in the art in view of the disclosureherein.

BRIEF DESCRIPTION OF THE FIGURES

[0102]FIG. 1 presents a schematic diagram showing A and B biosensorsignals, “B/A” averages and additional “moving average” (“A/B”)measurement cycles for 6-readings-per-hour processing versus3-readings-per-hour processing.

[0103]FIG. 2 presents a schematic of an exploded view of exemplarycomponents comprising one embodiment of an autosensor for use in amonitoring system.

[0104]FIGS. 3A, 3B, and 3C illustrate three different read frequenciesschemes ranging from serial paired measurements (AB, AB, AB; FIG. 3A),to a “rolling value” measurement (AB, BA, AB, BA; FIG. 3B), to an“integral split” measurement, where readings are provided mostfrequently (FIG. 3C).

[0105]FIG. 4A illustrates how the newest trapezoidal segment replacesthe oldest one as a new current value is taken in the integral splittingmethod. FIG. 4B illustrates the increase in reading frequency forvarious measurement methods (e.g., integral splitting and rolling valuesrelative to serial paired).

[0106]FIG. 5 illustrates a situation wherein analyte readings are missedby an analyte monitoring system following a failed recalibrationattempt, until a successful recalibration is performed.

[0107]FIG. 6 illustrates a situation wherein analyte readings are notmissed by an analyte monitoring system following a failed recalibrationattempt because the system reverts to using a previous calibration untila successful recalibration is performed.

DETAILED DESCRIPTION OF THE INVENTION

[0108] All publications, patents and patent applications cited hereinare hereby incorporated by reference in their entireties.

[0109] 1. Definitions

[0110] It is to be understood that the terminology used herein is forthe purpose of describing particular embodiments only, and is notintended to be limiting. As used in this specification and the appendedclaims, the singular forms “a”, “an” and “the” include plural referentsunless the context clearly dictates otherwise. Thus, for example,reference to “a reservoir” includes a combination of two or more suchreservoirs, reference to “an analyte” includes mixtures of analytes, andthe like.

[0111] Unless defined otherwise, all technical and scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although other methodsand materials similar, or equivalent, to those described herein can beused in the practice of the present invention, the preferred materialsand methods are described herein.

[0112] In describing and claiming the present invention, the followingterminology will be used in accordance with the definitions set outbelow.

[0113] The term “microprocessor” refers to a computer processorcontained on an integrated circuit chip, such a processor may alsoinclude memory and associated circuits. A microprocessor may furthercomprise programmed instructions to execute or control selectedfunctions, computational methods, switching, etc. Microprocessors andassociated devices are commercially available from a number of sources,including, but not limited to, Cypress Semiconductor Corporation, SanJose, Calif.; IBM Corporation, White Plains, N.Y.; Applied MicrosystemsCorporation, Redmond, Wash.; Intel Corporation, Chandler, Ariz.; and,National Semiconductor, Santa Clara, Calif.

[0114] The terms “analyte” and “target analyte” are used to denote anyphysiological analyte of interest that is a specific substance orcomponent that is being detected and/or measured in a chemical,physical, enzymatic, or optical analysis. A detectable signal (e.g., achemical signal or electrochemical signal) can be obtained, eitherdirectly or indirectly, from such an analyte or derivatives thereof.Furthermore, the terms “analyte” and “substance” are usedinterchangeably herein, and are intended to have the same meaning, andthus encompass any substance of interest. In preferred embodiments, theanalyte is a physiological analyte of interest, for example, glucose, ora chemical that has a physiological action, for example, a drug orpharmacological agent.

[0115] A “sampling device,” “sampling mechanism” or “sampling system”refers to any device and/or associated method for obtaining a samplefrom a biological system for the purpose of determining theconcentration of an analyte of interest. Such “biological systems”include any biological system from which the analyte of interest can beextracted, including, but not limited to, blood, interstitial fluid,perspiration and tears. Further, a “biological system” includes bothliving and artificially maintained systems. The term “sampling”mechanism refers to extraction of a substance from the biologicalsystem, generally across a membrane such as the stratum corneum ormucosal membranes, wherein said sampling is invasive, minimallyinvasive, semi-invasive or non-invasive. The membrane can be natural orartificial, and can be of plant or animal nature, such as natural orartificial skin, blood vessel tissue, intestinal tissue, and the like.Typically, the sampling mechanism is in operative contact with a“reservoir,” or “collection reservoir,” wherein the sampling mechanismis used for extracting the analyte from the biological system into thereservoir to obtain the analyte in the reservoir. Non-limiting examplesof sampling techniques include iontophoresis, sonophoresis (see, e.g.,International Publication No. WO 91/12772, published Sep. 5, 1991; U.S.Pat. No. 5,636,632), suction, electroporation, thermal poration, passivediffusion (see, e.g., International Publication Nos.: WO 97/38126(published Oct. 16, 1997); WO 97/42888, WO 97/42886, WO 97/42885, and WO97/42882 (all published Nov. 20, 1997); and WO 97/43962 (published Nov.27, 1997)), microfine (miniature) lances or cannulas, biolistic (e.g.,using particles accelerated to high speeds), subcutaneous implants orinsertions, and laser devices (see, e.g., Jacques et al. (1978) J.Invest. Dermatology 88:88-93; International Publication WO 99/44507,published Sep. 10, 1999; International Publication WO 99/44638,published Sep. 10, 1999; and International Publication WO 99/40848,published Aug. 19, 1999). Iontophoretic sampling devices are described,for example, in International Publication No. WO 97/24059, publishedJul. 10, 1997; European Patent Application EP 0942 278, published Sep.15, 1999; International Publication No. WO 96/00110, published Jan. 4,1996; International Publication No. WO 97/10499, published Mar. 2, 1997;U.S. Pat. Nos. 5,279,543; 5,362,307; 5,730,714; 5,771,890; 5,989,409;5,735,273; 5,827,183; 5,954,685 and 6,023,629, 6,298,254, all of whichare herein incorporated by reference in their entireties. Further, apolymeric membrane may be used at, for example, the electrode surface toblock or inhibit access of interfering species to the reactive surfaceof the electrode.

[0116] The term “physiological fluid” refers to any desired fluid to besampled, and includes, but is not limited to, blood, cerebrospinalfluid, interstitial fluid, semen, sweat, saliva, urine and the like.

[0117] The term “artificial membrane” or “artificial surface,” refersto, for example, a polymeric membrane, or an aggregation of cells ofmonolayer thickness or greater which are grown or cultured in vivo or invitro, wherein said membrane or surface functions as a tissue of anorganism but is not actually derived, or excised, from a pre-existingsource or host.

[0118] A “monitoring system” refers to a system useful for obtainingfrequent measurements of a physiological analyte present in a biologicalsystem. Such a system may comprise, but is not limited to, a samplingmechanism, a sensing mechanism, and a microprocessor mechanism inoperative communication with the sampling mechanism and the sensingmechanism.

[0119] A “measurement cycle” typically comprises extraction of ananalyte from a subject, using, for example, a sampling device, andsensing of the extracted analyte, for example, using a sensing device,to provide a measured signal, for example, a measured signal responsecurve. A complete measurement cycle may comprise one or more sets ofextraction and sensing.

[0120] The term “frequent measurement” refers to a series of two or moremeasurements obtained from a particular biological system, whichmeasurements are obtained using a single device maintained in operativecontact with the biological system over a time period in which a seriesof measurements (e.g, second, minute or hour intervals) is obtained. Theterm thus includes continual and continuous measurements.

[0121] The term “subject” encompasses any warm-blooded animal,particularly including a member of the class Mammalia such as, withoutlimitation, humans and nonhuman primates such as chimpanzees and otherapes and monkey species; farm animals such as cattle, sheep, pigs, goatsand horses; domestic mammals such as dogs and cats; laboratory animalsincluding rodents such as mice, rats and guinea pigs, and the like. Theterm does not denote a particular age or sex and, thus, includes adultand newborn subjects, whether male or female.

[0122] The term “transdermal” includes both transdermal and transmucosaltechniques, i.e., extraction of a target analyte across skin, e.g.,stratum corneum, or mucosal tissue. Aspects of the invention which aredescribed herein in the context of “transdermal,” unless otherwisespecified, are meant to apply to both transdermal and transmucosaltechniques.

[0123] The term “transdermal extraction,” or “transdermally extracted”refers to any sampling method, which entails extracting and/ortransporting an analyte from beneath a tissue surface across skin ormucosal tissue. The term thus includes extraction of an analyte using,for example, iontophoresis (reverse iontophoresis), electroosmosis,sonophoresis, microdialysis, suction, and passive diffusion. Thesemethods can, of course, be coupled with application of skin penetrationenhancers or skin permeability enhancing technique such as varioussubstances or physical methods such as tape stripping or pricking withmicro-needles. The term “transdermally extracted” also encompassesextraction techniques which employ thermal poration, lasermicroporation, electroporation, microfine lances, microfine cannulas,subcutaneous implants or insertions, combinations thereof, and the like.

[0124] The term “iontophoresis” refers to a method for transportingsubstances across tissue by way of an application of electrical energyto the tissue. In conventional iontophoresis, a reservoir is provided atthe tissue surface to serve as a container of (or to provide containmentfor) material to be transported. lontophoresis can be carried out usingstandard methods known to those of skill in the art, for example byestablishing an electrical potential using a direct current (DC) betweenfixed anode and cathode “iontophoretic electrodes,” alternating a directcurrent between anode and cathode iontophoretic electrodes, or using amore complex waveform such as applying a current with alternatingpolarity (AP) between iontophoretic electrodes (so that each electrodeis alternately an anode or a cathode). For example, see U.S. Pat. Nos.5,771,890 and 6,023,629 and PCT Publication No. WO 96/00109, publishedJan. 4, 1996.

[0125] The term “reverse iontophoresis” refers to the movement of asubstance from a biological fluid across a membrane by way of an appliedelectric potential or current. In reverse iontophoresis, a reservoir isprovided at the tissue surface to receive the extracted material, asused in the GlucoWatch® (Cygnus, Inc., Redwood City, Calif.) biographerglucose monitor (See, e.g., Tamada et al. (1999) JAMA 282:1839-1844).

[0126] “Electroosmosis” refers to the movement of a substance through amembrane by way of an electric field-induced convective flow. The termsiontophoresis, reverse iontophoresis, and electroosmosis, will be usedinterchangeably herein to refer to movement of any ionically charged oruncharged substance across a membrane (e.g., an epithelial membrane)upon application of an electric potential to the membrane through anionically conductive medium.

[0127] The term “sensing device,” or “sensing mechanism,” encompassesany device that can be used to measure the concentration or amount of ananalyte, or derivative thereof, of interest. Preferred sensing devicesfor detecting blood analytes generally include electrochemical sensordevices, optical sensor devices, chemical sensor devices, andcombinations thereof. Examples of electrochemical devices include theClark electrode system (see, e.g., Updike, et al., (1967) Nature214:986-988), and other amperometric, coulometric, or potentiometricelectrochemical devices, as well as, optical methods, for example UVdetection or infrared detection (e.g., U.S. Pat. No. 5,747,806). Othersensing devices include, but are not limited to, implanted sensingdevices (e.g., in a body cavity, blood vessel, under skin, or under asurface tissue layer).

[0128] A “biosensor” or “biosensor device” includes, but is not limitedto, a “sensor element” that includes, but is not limited to, a“biosensor electrode” or “sensing electrode” or “working electrode”which refers to the electrode that is monitored to determine the amountof electrical signal at a point in time or over a given time period,which signal is then correlated with the concentration of a chemicalcompound. The sensing electrode comprises a reactive surface whichconverts the analyte, or a derivative thereof, to electrical signal. Thereactive surface can be comprised of any electrically conductivematerial such as, but not limited to, platinum-group metals (including,platinum, palladium, rhodium, ruthenium, osmium, and iridium), nickel,copper, and silver, as well as, oxides, and dioxides, thereof, andcombinations or alloys of the foregoing, which may include carbon aswell. Some catalytic materials, membranes, and fabrication technologiessuitable for the construction of amperometric biosensors are describedby Newman, J. D., et al.(1995) Analytical Chemistry 67:4594-4599. Asensing device may, for example, comprises one or more sensingelectrodes. Alternately, a sensing device may, for example, comprise twoor more sensing electrodes. In a further embodiment, a sensing devicemay, for example, comprise an array of sensing electrodes comprisinggreater than two electrodes.

[0129] The “sensor element” can include components in addition to thesensing electrode, for example, it can include a “reference electrode”and a “counter electrode.” The term “reference electrode” is used tomean an electrode that provides a reference potential, e.g., a potentialcan be established between a reference electrode and a workingelectrode. The term “counter electrode” is used to mean an electrode inan electrochemical circuit that acts as a current source or sink tocomplete the electrochemical circuit. Although it is not essential thata counter electrode be employed where a reference electrode is includedin the circuit and the electrode is capable of performing the functionof a counter electrode, it is preferred to have separate counter andreference electrodes because the reference potential provided by thereference electrode is most stable when it is at equilibrium. If thereference electrode is required to act further as a counter electrode,the current flowing through the reference electrode may disturb thisequilibrium. Consequently, separate electrodes functioning as counterand reference electrodes are preferred.

[0130] In one embodiment, the “counter electrode” of the “sensorelement” comprises a “bimodal electrode.” The term “bimodal electrode”typically refers to an electrode which is capable of functioningnon-simultaneously as, for example, both the counter electrode (of the“sensor element”) and the iontophoretic electrode (of the “samplingmechanism”) as described, for example, U.S. Pat. No. 5,954,685.

[0131] The terms “reactive surface,” and “reactive face” are usedinterchangeably herein to mean the surface of the sensing electrodethat: (1) is in contact with the surface of an ionically conductivematerial which contains an analyte or through which an analyte, or aderivative thereof, flows from a source thereof; (2) is comprised of acatalytic material (e.g., a platinum group metal, platinum, palladium,rhodium, ruthenium, or nickel and/or oxides, dioxides and combinationsor alloys thereof) or a material that provides sites for electrochemicalreaction; (3) converts a chemical signal (for example, hydrogenperoxide) into an electrical signal (e.g., an electrical current); and(4) defines the electrode surface area that, when composed of a reactivematerial, is sufficient to drive the electrochemical reaction at a ratesufficient to generate a detectable, reproducibly measurable, electricalsignal when an appropriate electrical bias is supplied, that iscorrelatable with the amount of analyte present in the electrolyte.

[0132] An “ionically conductive material” refers to any material thatprovides ionic conductivity, and through which electrochemically activespecies can diffuse. The ionically conductive material can be, forexample, a solid, liquid, or semi-solid (e.g., in the form of a gel)material that contains an electrolyte, which can be composed primarilyof water and ions (e.g., sodium chloride), and generally comprises 50%or more water by weight. The material can be in the form of a hydrogel,a sponge or pad (e.g., soaked with an electrolytic solution), or anyother material that can contain an electrolyte and allow passage ofelectrochemically active species, especially the analyte of interest.Some exemplary hydrogel formulations are described in WO 97/02811,published Jan. 30, 1997, and WO 0064533A1, published Nov. 2, 2000, bothherein incorporated by reference. The ionically conductive material maycomprise a biocide. For example, during manufacture of an autosensorassembly, one or more biocides may be incorporated into the ionicallyconductive material. Biocides of interest include, but are not limitedto, compounds such as chlorinated hydrocarbons; organometallics;hydrogen releasing compounds; metallic salts; organic sulfur compounds;phenolic compounds (including, but not limited to, a variety of NipaHardwicke Inc. liquid preservatives registered under the trade namesNipastat®, Nipaguard®, Phenosept®, Phenonip®, Phenoxetol®, andNipacide®); quaternary ammonium compounds; surfactants and othermembrane-disrupting agents (including, but not limited to, undecylenicacid and its salts), combinations thereof, and the like.

[0133] The term “buffer” refers to one or more components which areadded to a composition in order to adjust or maintain the pH of thecomposition.

[0134] The term “electrolyte” refers to a component of the ionicallyconductive medium which allows an ionic current to flow within themedium. This component of the ionically conductive medium can be one ormore salts or buffer components, but is not limited to these materials.

[0135] The term “collection reservoir” is used to describe any suitablecontainment method or device for containing a sample extracted from abiological system. For example, the collection reservoir can be areceptacle containing a material which is ionically conductive (e.g.,water with ions therein), or alternatively it can be a material, such asa sponge-like material or hydrophilic polymer, used to keep the water inplace. Such collection reservoirs can be in the form of a hydrogel (forexample, in the shape of a disk or pad). Hydrogels are typicallyreferred to as “collection inserts.” Other suitable collectionreservoirs include, but are not limited to, tubes, vials, strips,capillary collection devices, cannulas, and miniaturized etched, ablatedor molded flow paths.

[0136] A “collection insert layer” is a layer of an assembly or laminatecomprising a collection reservoir (or collection insert) located, forexample, between a mask layer and a retaining layer.

[0137] A “laminate” refers to structures comprised of, at least, twobonded layers. The layers may be bonded by welding or through the use ofadhesives. Examples of welding include, but are not limited to, thefollowing: ultrasonic welding, heat bonding, and inductively coupledlocalized heating followed by localized flow. Examples of commonadhesives include, but are not limited to, chemical compounds such as,cyanoacrylate adhesives, and epoxies, as well as adhesives having suchphysical attributes as, but not limited to, the following: pressuresensitive adhesives, thermoset adhesives, contact adhesives, and heatsensitive adhesives.

[0138] A “collection assembly” refers to structures comprised of severallayers, where the assembly includes at least one collection insertlayer, for example a hydrogel. An example of a collection assembly asreferred to in the present invention is a mask layer, collection insertlayer, and a retaining layer where the layers are held in appropriatefunctional relationship to each other but are not necessarily a laminate(i.e., the layers may not be bonded together. The layers may, forexample, be held together by interlocking geometry or friction).

[0139] The term “mask layer” refers to a component of a collectionassembly that is substantially planar and typically contacts both thebiological system and the collection insert layer. See, for example,U.S. Pat. Nos. 5,735,273, 5,827,183, 6,141,573, and 6,201,979, allherein incorporated by reference.

[0140] The term “gel retaining layer” or “gel retainer” refers to acomponent of a collection assembly that is substantially planar andtypically contacts both the collection insert layer and the electrodeassembly.

[0141] The term “support tray” typically refers to a rigid,substantially planar platform and is used to support and/or align theelectrode assembly and the collection assembly. The support trayprovides one way of placing the electrode assembly and the collectionassembly into the sampling system.

[0142] An “autosensor assembly” refers to a structure generallycomprising a mask layer, collection insert layer, a gel retaining layer,an electrode assembly, and a support tray. The autosensor assembly mayalso include liners where the layers are held in approximate, functionalrelationship to each other. Exemplary collection assemblies andautosensor structures are described, for example, in InternationalPublication WO 99/58190, published Nov. 18, 1999; and U.S. Pat. Nos.5,735,273, 5,827,183, 6,141,573, and 6,201,979. The mask and retaininglayers are preferably composed of materials that are substantiallyimpermeable to the analyte (chemical signal) to be detected; however,the material can be permeable to other substances. By “substantiallyimpermeable” is meant that the material reduces or eliminates chemicalsignal transport (e.g., by diffusion). The material can allow for a lowlevel of chemical signal transport, with the proviso that chemicalsignal passing through the material does not cause significant edgeeffects at the sensing electrode.

[0143] The terms “about” or “approximately” when associated with anumeric value refers to that numeric value plus or minus 10 units ofmeasure (i.e. percent, grams, degrees or volts), preferably plus orminus 5 units of measure, more preferably plus or minus 2 units ofmeasure, most preferably plus or minus 1 unit of measure.

[0144] By the term “printed” is meant a substantially uniform depositionof an electrode formulation onto one surface of a substrate (i.e., thebase support). It will be appreciated by those skilled in the art that avariety of techniques may be used to effect substantially uniformdeposition of a material onto a substrate, e.g., Gravure-type printing,extrusion coating, screen coating, spraying, painting, electroplating,laminating, or the like.

[0145] The term “physiological effect” encompasses effects produced inthe subject that achieve the intended purpose of a therapy. In preferredembodiments, a physiological effect means that the symptoms of thesubject being treated are prevented or alleviated. For example, aphysiological effect would be one that results in the prolongation ofsurvival in a patient.

[0146] “Parameter” refers to an arbitrary constant or variable soappearing in a mathematical expression that changing it give variouscases of the phenomenon represented (McGraw-Hill Dictionary ofScientific and Technical Terms, S. P. Parker, ed., Fifth Edition,McGraw-Hill Inc., 1994). In the context of the GlucoWatch biographer, aparameter is a variable that influences the value of the blood glucoselevel as calculated by an algorithm.

[0147] “Decay” refers to a gradual reduction in the magnitude of aquantity, for example, a current detected using a sensor electrode wherethe current is correlated to the concentration of a particular analyteand where the detected current gradually reduces but the concentrationof the analyte does not.

[0148] “Skip” or “skipped” signals refer to data that do not conform topredetermined criteria (for example, error-associated criteria asdescribed in U.S. Pat. No. 6,233,471, herein incorporated by reference).A skipped reading, signal, or measurement value typically has beenrejected (i.e., a “skip error” generated) as not being reliable or validbecause it does not conform with data integrity checks, for example,where a signal is subjected to a data screen which invalidates incorrectsignals based on a detected parameter indicative of a poor or incorrectsignal.

[0149] 2. General Overview of the Inventions

[0150] Before describing the present invention in detail, it is to beunderstood that this invention is not limited to particular types ofmicroprocessors, monitoring systems, computational methods or processparameters, as use of such particulars may be selected in view of theteachings of the present specification. It is also to be understood thatthe terminology used herein is for the purpose of describing particularembodiments of the invention only, and is not intended to be limiting.

[0151] In one aspect the present invention relates to methods toincrease the number of analyte-related signals used to provide analytemeasurement values. Such analyte measurements may, for example, bechemical, physical, enzymatic, or optical. In one embodiment suchanalyte measurements are electrochemical, providing, for example,current and/or charge signals related to analyte amount orconcentration. This aspect of the present invention typically applies tothe situation where two or more analyte-related signals are used toobtain a single analyte measurement value, for example, the sum of twoor more values may be correlated to an analyte amount or concentration,or an average of two or more values may be correlated to an analyteamount or concentration.

[0152] For example, in a two sensor system where analyte-related signalsare serially obtained from each sensor in an alternating fashion, ananalyte-related signal from the first sensor (S₁) may be summed with ananalyte-related signal from the second sensor (S₂) to obtain a firstanalyte measurement value (M₁). The measurement cycle is repeated toobtain further analyte-related signals (e.g., S₃, S₄, S₅, S₆, etc.). Inthis example, S₃ and S₄ provide M₂, S₅ and S₆ provide M₃, etc. However,when applying the method of the present invention, the number ofanalyte-related measurement values is doubled. In the method of theinvention each analyte-related signal is paired with its next neighborto obtain a analyte measurement value, for example, S₁ and S₂ provideM₁, S₂ and S₃, provide M₂, S₃ and S₄ provide M₃, S₄ and S₅, provide M₄etc. Thus the number of analyte-related measurement values is increased(in this example, essentially doubled).

[0153] In another aspect of the present invention, each analyte-relatedsignal may be combined with one (or more) near or next neighbor toobtain, e.g., an average (or summed) analyte measurement value, i.e.,the average (or summed value) may be obtained using more than twoanalyte-related signals. The number of analyte-related signals that areused to obtain, e.g., an average (or summed) value may be empiricallydetermined by one of ordinary skill in the art following the guidance ofthe present specification. Generally, the averaged (or summed)analyte-related signal should be concordant with the trend of themeasured analyte-related signals.

[0154] Another example involves a single sensor system. In this example,the analyte-related signals are serially obtained from a single sensor,for example, a first analyte-related signal (S₁), a secondanalyte-related signal (S₂), S₃, S₄, S₅, etc. The analyte relatedsignals may be paired to obtain an analyte measurement value, forexample, S₁ and S₂ provide M₁, S₃ and S₄provide M₂, etc. In this case,the number of analyte-related measurement values may be increased by themethod of the present invention by pairing each analyte-related signalwith its next neighbor to obtain a analyte measurement value, forexample, S₁ and S₂ provide M₁, S₂ and S₃, provide M₂, S₃ and S₄ provideM₃, S₄ and S₅, provide M₄, etc.

[0155] The present invention also relates to methods of increasing thenumber of analyte measurement values related to the amount orconcentration of an analyte in a subject as measured using an analytemonitoring device. In this method a series of analyte-related signals isobtained from the analyte monitoring device over time. Typically, two ormore contiguous analyte-related signals are used to obtain a singleanalyte measurement value (M). In this method, paired analyte-relatedsignals are typically used to calculate the measurement value. Oneimprovement provided by the present method is that, prior to the presentmethod, such an analyte monitoring device typically used paired signalsto obtain a single measurement value; but an analyte-related signal fromthe monitoring device was not typically used to calculate more than oneanalyte measurement value. In the present method, the two or morecontiguous analyte-related signals, used to obtain the single analytemeasurement value, comprise first and last analyte-related signals ofthe series.

[0156] The method involves mathematically computing rolling analytemeasurement values, wherein (i) each rolling analyte measurement valueis calculated based on two or more contiguous analyte-related signalsfrom the series of analyte-related signals obtained from the analytemonitoring device. Subsequent rolling analyte measurement values aremathematically computed by dropping the first analyte-related signalfrom the previous rolling analyte measurement value and including ananalyte-related signal contiguous and subsequent to the lastanalyte-related signal used to calculate the previous rolling analytemeasurement value. Further rolling analyte measurement values areobtained by repeating the dropping of the first analyte-related signalused to calculate the previous rolling analyte measurement and includingan analyte-related signal contiguous and subsequent to the lastanalyte-related signal used to calculate the previous rolling analytemeasurement. Each rolling analyte measurement value provides ameasurement related to the amount or concentration of analyte in thesubject. By employing this method the number of analyte measurementvalues, derived from the analyte-related signals in the series ofanalyte-related signals obtained from the analyte monitoring device, isincreased by serially calculating rolling analyte measurement values.

[0157] In one embodiment of this aspect of the invention, the rollinganalyte measurement value is, for example, an average of two or moreanalyte-related signals; alternately, the rolling analyte measurementvalue is a sum of two or more analyte-related signals. In anotherembodiment, each analyte-related signal is represented by an integralover time, and the rolling analyte measurement value is obtained byintegral splitting.

[0158] Missing or error-associated signals in the series ofanalyte-related signals obtained from the analyte monitoring device maybe estimated using interpolation before mathematically computing rollinganalyte measurement values. Such missing or error-associated signals mayalso be estimated using extrapolation before mathematically computingrolling analyte measurement values.

[0159] In a preferred embodiment, the analyte is glucose. In oneembodiment, the analyte monitoring device comprises (i) an iontophoreticsampling device, and (ii) an electrochemical sensing device. Theanalyte-related signal may, for example, be a current or a chargerelated to analyte amount or concentration of analyte in the subject.

[0160] Other embodiments of the present invention will be clear to oneof ordinary skill in the art in view of the teachings disclosed herein.

[0161] In another aspect of the present invention, interpolation and/orextrapolation are used to estimate unusable, missing or error-associatedanalyte-related signals. Such signals may be unusable for a variety ofreasons, typically where an error has been detected that places ananalyte-related signal in question. In the interpolation aspect, aprevious analyte-related signal and a subsequent analyte-related signalare used to estimate the intervening analyte-related signal. Further,one or more previous analyte-related signals, and/or one or moresubsequent analyte-related signals may also be employed forinterpolation. In the extrapolation aspect, two previous analyte-relatedsignals are used to estimate a subsequent analyte-related signal.Further, two or more previous analyte-related signals may also beemployed for extrapolation. Interpolation and extrapolation of values isalso employed in another aspect of the present invention that reducesthe incident of failed calibrations.

[0162] One embodiment of this second aspect of the present inventionincludes a method of replacing unusable analyte-related signals whenemploying an analyte monitoring device to measure an analyte amount orconcentration in a subject. A series of analyte-related signals,obtained from the analyte monitoring device over time, is providedwherein each analyte-related signal is related to the amount orconcentration of analyte in the subject. An unusable analyte-relatedsignal is replaced with an estimated signal, for example, by either:

[0163] (A) if one or more analyte-related signals previous to theunusable analyte-related signal and one or more analyte-related signalssubsequent to the unusable analyte related signal are available, theninterpolation is used to estimate the unusable, interveninganalyte-related signal; or

[0164] (B) if two or more analyte-related signals previous to theunusable analyte-related signal are available, then extrapolation isused to estimate the unusable, subsequent analyte-related signal.

[0165] In this method, the analyte monitoring device may comprise one ormore sensor devices and a relationship between the signals obtained fromthe different sensor devices is used in interpolation and/orextrapolation calculation of estimated values. In one embodiment, thesensor device comprises two sensor elements and a ratio of signalsobtained from a first sensor relative to a second sensor is employed ininterpolation and/or extrapolation calculation of estimated signalvalues.

[0166] In a further aspect of the present invention, methods aredescribed for reducing the incidence of failed calibration for ananalyte monitoring system that is used to monitor an amount orconcentration of analyte present in a subject.

[0167] In another aspect of the present invention, methods are describedfor providing an alert related to analyte values exceeding predeterminedthresholds (e.g., high and/or low thresholds) or ranges of values. Inthis aspect of the invention a gradient method and/or predictivealgorithm method may be used.

[0168] One or more microprocessors may be utilized to mathematicallycompute or control execution of algorithms related to any and all of themethods described herein. Further, such one or more microprocessors maybe used to control operation of the components of the analyte monitoringsystem (e.g., a sampling device and/or a sensing device of themonitoring system). In addition, the one or more microprocessors maycontrol operation of other components, further algorithms, calculations,and/or the providing of alerts to a subject (user of the analytemonitoring system).

[0169] The present invention also includes analyte monitoring devicesemploying the above methods.

[0170] Although a number of methods and materials similar or equivalentto those described herein can be used in the practice of the presentinvention, some preferred materials and methods are described herein.

[0171] 3. Exemplary Monitoring Systems

[0172] Numerous analyte monitoring systems can be used in the practiceof the present invention. Typically, the monitoring system used tomonitor the level of a selected analyte in a target system comprises asampling device, which provides a sample comprising the analyte, and asensing device, which detects the amount or concentration of the analyteor a signal associated with the analyte amount or concentration in thesample.

[0173] One exemplary monitoring system (the GlucoWatch biographer) isdescribed herein for monitoring glucose levels in a biological systemvia the transdermal extraction of glucose from the biological system,particularly an animal subject, and then detection of signalcorresponding to the amount or concentration of the extracted glucose.Transdermal extraction is carried out by applying an electrical currentor ultrasonic radiation to a tissue surface at a collection site. Theelectrical current is used to extract small amounts of glucose from thesubject into a collection reservoir. The collection reservoir is incontact with a sensor element (e.g., a biosensor) which provides formeasurement of glucose concentration in the subject. As glucose istransdermally extracted into the collection reservoir, the analytereacts with the glucose oxidase within the reservoir to produce hydrogenperoxide. The presence of hydrogen peroxide generates a current at thebiosensor electrode that is directly proportional to the amount ofhydrogen peroxide in the reservoir. This current provides a signal whichcan be detected and interpreted (for example, employing a selectedalgorithm) by an associated system controller to provide a glucoseconcentration value or amount for display.

[0174] In the use of the sampling system, a collection reservoir iscontacted with a tissue surface, for example, on the stratum corneum ofa subject's skin. An electrical current is then applied to the tissuesurface in order to extract glucose from the tissue into the collectionreservoir. Extraction is carried out, for example, frequently over aselected period of time. The collection reservoir is analyzed, at leastperiodically and typically frequently, to measure glucose concentrationtherein. The measured value correlates with the subject's blood glucoselevel.

[0175] To sample the analyte, one or more collection reservoirs areplaced in contact with a tissue surface on a subject. The ionicallyconductive material within the collection reservoir is also in contactwith an electrode (for reverse iontophoretic extraction) which generatesa current sufficient to extract glucose from the tissue into thecollection reservoir. Referring to FIG. 2, an exploded view of exemplarycomponents comprising one embodiment of an autosensor for use in aniontophoretic sampling system is presented. The autosensor componentsinclude two biosensor/iontophoretic electrode assemblies, 104 and 106,each of which have an annular iontophoretic electrode, respectivelyindicated at 108 and 110, which encircles a biosensor electrode 112 and114. The electrode assemblies 104 and 106 are printed onto a polymericsubstrate 116 which is maintained within a sensor tray 118. A collectionreservoir assembly 120 is arranged over the electrode assemblies,wherein the collection reservoir assembly comprises two hydrogel inserts122 and 124 retained by a gel retaining layer 126 and mask layer 128.Further release liners may be included in the assembly, for example, apatient liner 130, and a plow-fold liner 132. In an alternativeembodiment, the electrode assemblies can include bimodal electrodes. Amask layer 128 (for example, as described in PCT Publication No. WO97/10356, published Mar. 20, 1997, and U.S. Pat. Nos. 5,735,273,5,827,183, 6,141,573, and 6,201,979, all herein incorporated byreference) may be present. Other autosensor embodiments are described inWO 99/58190, published Nov. 18, 1999, herein incorporated by reference.

[0176] The mask and retaining layers are preferably composed ofmaterials that are substantially impermeable to the analyte (e.g.,glucose) to be detected (see, for example, U.S. Pat. Nos. 5,735,273, and5,827,183, both herein incorporated by reference). By “substantiallyimpermeable” is meant that the material reduces or eliminates analytetransport (e.g., by diffusion). The material can allow for a low levelof analyte transport, with the proviso that the analyte that passesthrough the material does not cause significant edge effects at thesensing electrode used in conjunction with the mask and retaininglayers. Examples of materials that can be used to form the layersinclude, but are not limited to, polyester, polyester derivatives, otherpolyester-like materials, polyurethane, polyurethane derivatives andother polyurethane-like materials.

[0177] The components shown in exploded view in FIG. 2 are intended foruse in a automatic sampling system which is configured to be worn likean ordinary wristwatch, as described, for example, in PCT PublicationNo. WO 96/00110, published Jan. 4, 1996, herein incorporated byreference. The wristwatch housing can further include suitableelectronics (e.g., one or more microprocessor(s), memory, display andother circuit components) and power sources for operating the automaticsampling system. The one or more microprocessors may control a varietyof functions, including, but not limited to, control of a samplingdevice, a sensing device, aspects of the measurement cycle (for example,timing of sampling and sensing, and alternating polarity betweenelectrodes), connectivity, computational methods, different aspects ofdata manipulation (for example, acquisition, recording, recalling,comparing, and reporting), etc.

[0178] The sensing electrode can be, for example, a Pt-comprisingelectrode configured to provide a geometric surface area of about 0.1 to3 cm², preferably about 0.5 to 2 cm², and more preferably about 1 cm².This particular configuration is scaled in proportion to the collectionarea of the collection reservoir used in the sampling system of thepresent invention, throughout which the extracted analyte and/or itsreaction products will be present. The electrode composition isformulated using analytical- or electronic-grade reagents and solventswhich ensure that electrochemical and/or other residual contaminants areavoided in the final composition, significantly reducing the backgroundnoise inherent in the resultant electrode. In particular, the reagentsand solvents used in the formulation of the electrode are selected so asto be substantially free of electrochemically active contaminants (e.g.,anti-oxidants), and the solvents in particular are selected for highvolatility in order to reduce washing and cure times. Some electrodeembodiments are described in European Patent Publication 0 942 278 A2,published Sep. 15, 1999, herein incorporated by reference.

[0179] The reactive surface of the sensing electrode can be comprised ofany electrically conductive material such as, but not limited to,platinum-group metals (including, platinum, palladium, rhodium,ruthenium, osmium, and iridium), nickel, copper, silver, and carbon, aswell as, oxides, dioxides, combinations or alloys thereof. Somecatalytic materials, membranes, and fabrication technologies suitablefor the construction of amperometric biosensors were described byNewman, J. D., et al. (Analytical Chemistry 67(24), 4594-4599, 1995,herein incorporated by reference).

[0180] Any suitable iontophoretic electrode system can be employed, anexemplary system uses a silver/silver chloride (Ag/AgCl) electrodesystem. The iontophoretic electrodes are formulated typically using twoperformance criteria: (1) the electrodes are capable of operation forextended periods, preferably periods of up to 24 hours or longer; and(2) the electrodes are formulated to have high electrochemical purity inorder to operate within the present system which requires extremely lowbackground noise levels. The electrodes must also be capable of passinga large amount of charge over the life of the electrodes. With regard tooperation for extended periods of time, Ag/AgCl electrodes are capableof repeatedly forming a reversible couple which operates withoutunwanted electrochemical side reactions (which could give rise tochanges in pH, and liberation of hydrogen and oxygen due to waterhydrolysis). The Ag/AgCl electrode is thus formulated to withstandrepeated cycles of current passage in the range of about 0.01 to 1.0 mAper cm² of electrode area. With regard to high electrochemical purity,the Ag/AgCl components are dispersed within a suitable polymer binder toprovide an electrode composition which is not susceptible to attack(e.g., plasticization) by components in the collection reservoir, e.g.,the hydrogel composition. The electrode compositions are also typicallyformulated using analytical- or electronic-grade reagents and solvents,and the polymer binder composition is selected to be free ofelectrochemically active contaminants which could diffuse to thebiosensor to produce a background current.

[0181] Some exemplary sensors, electrodes, and electrode assemblies aredescribed, for example, in the following United States patents , allherein incorporated by reference in their entireties: U.S. Pat. Nos.5,954,685, 6,139,718, and 6,284,126.

[0182] The automatic sampling system can transdermally extract thesample over the course of a selected period of time using reverseiontophoresis. The collection reservoir comprises an ionicallyconductive medium, preferably the hydrogel medium described hereinabove.A first iontophoresis electrode is contacted with the collectionreservoir (which is typically in contact with a target, subject tissuesurface), and a second iontophoresis electrode is contacted with eithera second collection reservoir in contact with the tissue surface, orsome other ionically conductive medium in contact with the tissue. Apower source provides an electrical potential between the two electrodesto perform reverse iontophoresis in a manner known in the art. Asdiscussed above, the biosensor selected to detect the presence, andpossibly the level, of the target analyte (for example, glucose) withina reservoir is also in contact with the reservoir. Typically, there aretwo collections reservoirs, each comprising glucose oxidase, and each inoperative contact with iontophoretic electrode and a sensing electrode.The iontophoretic electrode may be a bimodal electrode that also serves,non-concurrently, as a counter electrode to the sensing electrode (see,for example, U.S. Pat. No. 5,954,685, herein incorporated by reference).

[0183] In practice, an electric potential (either direct current or amore complex waveform) is applied between the two iontophoresiselectrodes such that current flows from the first electrode through thefirst conductive medium into the skin, and back out from the skinthrough the second conductive medium to the second electrode. Thiscurrent flow extracts substances through the skin into the one or morecollection reservoirs through the process of reverse iontophoresis orelectroosmosis. The electric potential may be applied as described inPCT Publication No. WO 96/00110, published Jan. 4, 1996, hereinincorporated by reference. Typically, the electrical potential isalternated between two reservoirs to provide extraction of analyte intoeach reservoir in an alternating fashion (see, for example, U.S. Pat.Nos. 5,771,890, 6,023,629, 5,954,685, 6,298,254, all herein incorporatedby reference in their entireties). Analyte is also typically detected ineach reservoir.

[0184] As an example, to extract glucose, the applied electrical currentdensity on the skin or tissue can be in the range of about 0.01 to about2 mA/cm². In order to facilitate the extraction of glucose, electricalenergy can be applied to the electrodes, and the polarity of theelectrodes can be, for example, alternated so that each electrode isalternately a cathode or an anode. The polarity switching can be manualor automatic. A device and method for sampling of substances usingalternating polarity is described in U.S. Pat. No. 5,771,890, issuedJun. 30, 1998, herein incorporated by reference.

[0185] When a bimodal electrode is used (e.g., U.S. Pat. No. 5,954,685,issued Sep. 21, 1999, herein incorporated by reference), during thereverse iontophoretic phase, a power source provides a current flow tothe first bimodal electrode to facilitate the extraction of the chemicalsignal into the reservoir. During the sensing phase, a separate powersource is used to provide voltage to the first sensing electrode todrive the conversion of chemical signal retained in reservoir toelectrical signal at the catalytic face of the sensing electrode. Theseparate power source also maintains a fixed potential at the electrodewhere, for example hydrogen peroxide is converted to molecular oxygen,hydrogen ions, and electrons, which is compared with the potential ofthe reference electrode during the sensing phase. While one sensingelectrode is operating in the sensing mode it is electrically connectedto the adjacent bimodal electrode which acts as a counter electrode atwhich electrons generated at the sensing electrode are consumed.

[0186] The electrode subassembly can be operated by electricallyconnecting the bimodal electrodes such that each electrode is capable offunctioning as both an iontophoretic electrode and counter electrodealong with appropriate sensing electrode(s) and reference electrode(s).

[0187] A potentiostat is an electrical circuit used in electrochemicalmeasurements in three electrode electrochemical cells. A potential isapplied between the reference electrode and the sensing electrode. Thecurrent generated at the sensing electrode flows through circuitry tothe counter electrode (i.e., no current flows through the referenceelectrode to alter its equilibrium potential). Two independentpotentiostat circuits can be used to operate the two biosensors. For thepurpose of the present invention, the electrical current measured at thesensing electrode subassembly is the current that is correlated with anamount of chemical signal corresponding to the analyte.

[0188] The detected current can be correlated with the subject's bloodglucose concentration (e.g., using a statistical technique or algorithmor combination of techniques) so that the system controller may displaythe subject's actual blood glucose concentration as measured by thesampling system. Such statistical techniques can be formulated asalgorithm(s) and incorporated in one or more microprocessor(s)associated with the sampling system. Exemplary signal processingapplications include, but are not limited to, those taught in thefollowing U.S. Pat. Nos. 6,144,869, 6,233,471, 6,180,416, hereinincorporated by reference. Exemplary methods for analyte monitoringinclude, but are not limited to, those taught in the following U.S. Pat.Nos. 5,989,409, 6,144,869, 6,272,364, 6,299,578, and 6,309,351, allherein incorporated by reference.

[0189] In a further aspect of the present invention, thesampling/sensing mechanism and user interface may be found on separatecomponents (see, for example, WO 0047109A1, published Aug. 17, 2000).Thus, the monitoring system can comprise at least two components, inwhich a first component comprises sampling mechanism and sensingmechanism that are used to extract and detect an analyte, for example,glucose, and a second component that receives the analyte data from thefirst component, conducts data processing on the analyte data todetermine an analyte concentration and then displays the analyteconcentration data. Typically, microprocessor functions (e.g., controlof a sampling device, a sensing device, aspects of the measurementcycle, computational methods, different aspects of data manipulation orrecording, etc.) are found in both components. Alternatively,microprocessing components may be located in one or the other of the atleast two components. The second component of the monitoring system canassume many forms, including, but not limited to, the following: awatch, a credit card-shaped device (e.g., a “smart card” or “universalcard” having a built-in microprocessor as described for example in U.S.Pat. No. 5,892,661, herein incorporated by reference), a pager-likedevice, cell phone-like device, or other such device that communicatesinformation to the user visually, audibly, or kinesthetically.

[0190] Further, additional components may be added to the system, forexample, a third component comprising a display of analyte values or analarm related to analyte concentration, may be employed. In certainembodiments, a delivery unit is included in the system. An exemplarydelivery unit is an insulin delivery unit. Insulin delivery units, bothimplantable and external, are known in the art and described, forexample, in U.S. Pat. Nos. 5,995,860; 5,112,614 and 5,062,841, hereinincorporated by reference. Preferably, when included as a component ofthe present invention, the delivery unit is in communication (e.g.,wire-like or wireless communication) with the extracting and/or sensingmechanism such that the sensing mechanism can control the insulin pumpand regulate delivery of a suitable amount of insulin to the subject.

[0191] Advantages of separating the first component (e.g., including thebiosensor and iontophoresis functions) from the second component (e.g.,including some microprocessor and display functions) include greaterflexibility, discretion, privacy and convenience to the user. Having asmall and lightweight measurement unit allows placement of the twocomponents of the system on a wider range of body sites, for example,the first component may be placed on the abdomen or upper arm. Thiswider range of placement options may improve the accuracy throughoptimal extraction site selection (e.g., torso rather than extremities)and greater temperature stability (e.g., via the insulating effects ofclothing). Thus, the collection and sensing assembly will be able to beplaced on a greater range of body sites. Similarly, a smaller and lessobtrusive microprocessor and display unit (the second component)provides a convenient and discrete system by which to monitor analytes.The biosensor readouts and control signals will be relayed via wire-likeor wireless technology between the collection and sensing assembly andthe display unit which could take the form of a small watch, a pager, ora credit card-sized device. This system also provides the ability torelay an alert message or signal during nighttime use, for example, to asite remote from the subject being monitored.

[0192] In one embodiment, the two components of the device can be inoperative communication via a wire or cable-like connection. Operativecommunications between the components can be wireless link, i.e.provided by a “virtual cable,” for example, a telemetry link. Thiswireless link can be uni- or bi-directional between the two components.In the case of more than two components, links can be a combination ofwire-like and wireless.

[0193] 4. Exemplary Analytes

[0194] The analyte can be any one or more specific substance, component,or combinations thereof that one is desirous of detecting and/ormeasuring in a chemical, physical, enzymatic, or optical analysis.

[0195] Analytes that can be measured using the methods of the presentinvention include, but are not limited to, amino acids, enzymesubstrates or products indicating a disease state or condition, othermarkers of disease states or conditions, drugs of abuse (e.g., ethanol,cocaine), therapeutic and/or pharmacologic agents (e.g., theophylline,anti-HIV drugs, lithium, anti-epileptic drugs, cyclosporin,chemotherapeutics), electrolytes, physiological analytes of interest(e.g., urate/uric acid, carbonate, calcium, potassium, sodium, chloride,bicarbonate (CO₂), glucose, urea (blood urea nitrogen), lactate and/orlactic acid, hydroxybutyrate, cholesterol, triglycerides, creatine,creatinine, insulin, hematocrit, and hemoglobin), blood gases (carbondioxide, oxygen, pH), lipids, heavy metals (e.g., lead, copper), and thelike. Analytes in non-biological systems may also be evaluated using themethods of the present invention.

[0196] In preferred embodiments, the analyte is a physiological analyteof interest, for example glucose, or a chemical that has a physiologicalaction, for example a drug or pharmacological agent.

[0197] In order to facilitate detection of the analyte, an enzyme (orenzymes) can be disposed within the one or more collection reservoirs.The selected enzyme is capable of catalyzing a reaction with theextracted analyte to the extent that a product of this reaction can besensed, e.g., can be detected electrochemically from the generation of acurrent which current is,detectable and proportional to the amount ofthe analyte which is reacted. In one embodiment of the presentinvention, a suitable enzyme is glucose oxidase, which oxidizes glucoseto gluconic acid and hydrogen peroxide. The subsequent detection ofhydrogen peroxide on an appropriate biosensor electrode generates twoelectrons per hydrogen peroxide molecule creating a current that can bedetected and related to the amount of glucose entering the device.Glucose oxidase (GOx) is readily available commercially and has wellknown catalytic characteristics. However, other enzymes can also be usedsingly (for detection of individual analytes) or together (for detectionof multiple analytes), as long as they specifically catalyze a reactionwith an analyte or substance of interest to generate a detectableproduct in proportion to the amount of analyte so reacted.

[0198] In like manner, a number of other analyte-specific enzyme systemscan be used in the invention, which enzyme systems operate on much thesame general techniques. For example, a biosensor electrode that detectshydrogen peroxide can be used to detect ethanol using an alcohol oxidaseenzyme system, or similarly uric acid with urate oxidase system,cholesterol with a cholesterol oxidase system, and theophylline with axanthine oxidase system.

[0199] In addition, the oxidase enzyme (used for hydrogenperoxidase-based detection) can be replaced or complemented with anotherredox system, for example, the dehydrogenase-enzyme NAD-NADH, whichoffers a separate route to detecting additional analytes.Dehydrogenase-based sensors can use working electrodes made of gold orcarbon (via mediated chemistry). Examples of analytes suitable for thistype of monitoring include, but are not limited to, cholesterol,ethanol, hydroxybutyrate, phenylalanine, triglycerides, and urea.

[0200] Further, the enzyme can be eliminated and detection can rely ondirect electrochemical or potentiometric detection of an analyte. Suchanalytes include, without limitation, heavy metals (e.g., cobalt, iron,lead, nickel, zinc), oxygen, carbonate/carbon dioxide, chloride,fluoride, lithium, pH, potassium, sodium, and urea. Also, the samplingsystem described herein can be used for therapeutic drug monitoring, forexample, monitoring anti-epileptic drugs (e.g., phenytoin), chemotherapy(e.g., adriamycin), hyperactivity (e.g., ritalin), andanti-organ-rejection (e.g., cyclosporin).

[0201] Preferably, a sensor electrode is able to detect the analyte thathas been extracted into the one or more collection reservoirs whenpresent at nominal concentration levels. Suitable exemplary biosensorelectrodes and associated sampling systems as described in are describedin PCT Publication Nos. WO 97/10499, published Mar. 20, 1997, WO98/42252, published Oct. 1, 1998, U.S. Pat. No. 6,284,126, and U.S. Pat.No. 6,139,718, all herein incorporated by reference.

[0202] A single sensor may detect multiple analytes and/or reactionproducts of analytes. For example, a platinum sensor could be used todetect tyrosine and glucose in a single sample. The tyrosine isdetected, for example, by direct electrochemical oxidation at a suitableelectrode potential (e.g., approximately 0.6V vs. Ag/AgCl ). The glucoseis detected, e.g., using glucose oxidase and detecting the hydrogenperoxide reaction product.

[0203] Different sensing devices and/or sensing systems can be employedas well to distinguish between signals. For example, a first gelcontaining glucose oxidase associated with a first platinum sensor canbe used for the detection of glucose, while a second gel containinguricase associated with a second platinum sensor can be used for thedetection of urea.

[0204] 5. Methods To Increase The Number Of Analyte-Related Signals andImprove Usability

[0205] A. “Rolling Values”

[0206] In one aspect, the present invention relates to methods toincrease the number usable (i.e., good, as in not associated with asignificant error) analyte related signals. In one embodiment the methodprovides for obtaining a series of samples comprising the analyte ofinterest, e.g., glucose, from a subject (e.g., a mammal). This methodapplies, generally, to monitoring systems that provide a series ofanalyte-related signals over time.

[0207] The present invention relates generally to a method formonitoring an amount or concentration of analyte present in a subject,wherein a series of signals, over time, is provided, and each signal isrelated to the analyte amount or concentration in the subject. Multiplesignals are then combined to provide a “rolling value,” for example by:

[0208] calculating a series of average signals wherein (i) each averagesignal is calculated based on two or more contiguous signals (i.e.,signals next to or near in time or sequence to each other) in theseries, and (ii) each average signal provides a measurement related tothe amount or concentration of analyte in the subject; or

[0209] calculating a series of sums, wherein (i) each summed signal iscalculated based on two or more contiguous (i.e., next to or near intime or sequence) signals in the series, and (ii) each summed signalprovides a measurement related to the amount or concentration of analytein the subject. Missing signals in the series may be estimated usinginterpolation and/or extrapolation (discussed further below), and suchestimated signals can be used in said calculations.

[0210] In one embodiment the invention relates to the use of amonitoring system comprising two or more sensors determining theanalyte-related signals based on the same analyte. The method isdescribed below with reference to the use of two collection reservoirsinto which the analyte-containing samples are extracted. However, one ofordinary skill in the art, following the guidance of the presentspecification, could adapt this method for use with monitoring systemshaving one sensor or more than two sensors used to determineanalyte-related signals.

[0211] In this exemplary method, two analyte-related signals areobtained from two independent sensors. For example, sensors in contactwith extracted sample, comprising the analyte, are used to obtain asignal from each sample that is related to the analyte amount orconcentration in the subject. Repeated rounds of extraction and sensingprovide a series of signals. A sensing device may, for example, comprisefirst and second sensors, wherein the first sensor is in operativecontact with the first collection reservoir and the sensing providessignal S^(A) _(j) (where S is the signal, j is the time interval, forexample a measurement half-cycle where a full measurement cyclecomprises obtaining signal from sensor A and sensor B), and A denotesthat the signal is from sensor A), and the second sensor is in operativecontact with the second collection reservoir and the sensing providessignal S^(B) _(j+1) (where S is the signal, j+1 is the time interval,for example a measurement half-cycle where a full measurement cyclecomprises obtaining signal from sensor A and sensor B, e.g., a fullmeasurement cycle is (j)+(j+1)), and B denotes that the signal is fromsensor B).

[0212] Rather than basing the analyte-related measurement solely onS^(A)/S^(B) pairs of signals, analyte-related measurements can be basedon a rolling value of two (or more) signals in a series. For example,when using two contiguous signals, a series of rolling average signalsmay be calculated as follows:

(average signal)_(j)=(S ^(B) _(J−1) +S ^(A) _(j))/2,   Eqn. 1

[0213] where (J−1) is the measurement half-cycle previous to j;

(average signal)_(J+1)=(S ^(A) _(J) +S ^(B) _(J+1))/2;   Eqn. 2

(average signal)_(J+2)=(S ^(B) _(J+1) +S ^(A) _(J+2))/2,   Eqn. 3

[0214] where (J+2) is two measurement half-cycles after j; and, for eachprevious equation (Eqn.), wherein each average signal provides ameasurement related to the amount or concentration of analyte in thesubject. Calculation of further average signals at later (e.g., j+3,j+4, etc.) or earlier (e.g., j−1, j−2, etc.) times is accomplishedfollowing the procedure of the examples shown above.

[0215] Alternately, the sum or two or more signals may be related toanalyte amount or concentration. In this case, a series of rollingvalues may be calculated, for example, when using two contiguoussignals, as follows:

(summed signal)_(j)=(S ^(B) _(j−1) +S ^(A) _(J));   Eqn. 4

(summed signal)_(J+1)=(S ^(A) _(J) +S ^(B) _(J+1)); and   Eqn. 5

(summed signal)_(j+2)=(S ^(B) _(j+1) +S ^(A) _(J+2))   Eqn. 6

[0216] Calculation of further summed signals at later (e.g., j+3, j+4,etc.) or earlier (e.g., j−1, j−2, etc.) times is accomplished followingthe procedure of the examples shown above.

[0217] This method can be applied to data obtained using the GlucoWatchbiographer. The GlucoWatch biographer comprises two sensing electrodes,designated “A” and “B”, each in contact with a hydrogel. Current ispassed across A and B for iontophoretic extraction of glucose. Eachhydrogel is in contact with a sensor electrode, which provides signalsrelated to glucose amount or concentration in a sample. After a sampleis transdermally extracted into the hydrogel, the glucose in the samplereacts with the glucose oxidase within the hydrogel to produce hydrogenperoxide. The presence of hydrogen peroxide generates a current at thesensor electrode that is directly proportional to the amount of hydrogenperoxide in the hydrogel. This current provides a signal which can bedetected and interpreted (for example, employing a Mixtures of Expertsalgorithm, see, for example, U.S. Pat. Nos. 6,180,416, 6,326,160, hereinincorporated by reference in their entireties) by an associated systemcontroller to provide a glucose amount or concentration value fordisplay.

[0218] A current related to glucose amount or concentration in thesample (i.e., an analyte-related signal) is obtained at each sensorusing a biosensor. Current may be converted to charge by integration. Inthe first version of the GlucoWatch biographer, each measurement cycleconsisted of the following: 3 minutes of iontophoresis, followed by a 7minutes of measurement for the first half cycle, then the iontophoresiscurrent polarity was reversed and there was another 3 minutes ofiontophoresis followed by 7 minutes of biosensor measurement. Theglucose was drawn to the cathode, so in the first half cycle the “B”side collected glucose and in the second half cycle the “A” sidecollected glucose. The A and B measurements were averaged as one methodto achieve noise reduction. Accordingly, one hour's worth of datainvolved three full cycles of BA averages: BA, BA, BA (FIG. 1). Usingthe method of the present invention, the averaging is done with a “leapfrog” or rolling average approach, in which the last half cycle of ameasurement becomes the first half cycle of the next measurement. Onehour's worth of measurements can involve six full cycles of BA or ABaverages: BA, AB, BA, AB, BA AB (FIG. 1). Thus the method provides theadvantage of updating measurements more frequently to provide them tothe user. Therefore, in the case of no skip-reading errors, measurementsare reported based on signals from biosensors A and B measurement pairssuch as: BA, AB, BA, AB, and so on.

[0219] The configuration of the GlucoWatch biographer included sixextractions per hour, yet only three hourly readings. From anengineering standpoint, this represents a sub-optimum number of hourlyreadings. However, from the standpoint of a person with diabetes, andthe number of readings cannot be said to be a sub-optimum as threehourly readings represent an unprecedented amount of information forsubjects using the monitor. One solution to provide more hourly readingsis to change the sequence of the GlucoWatch biographer to computereadings more frequently. The method described above computes six hourlyreadings corresponding to the six extractions. Advantages of the methodsof the present invention for providing more measurement values include,but are not limited to, the following: allowing tighter screens forproviding data points having minimum error; providing highertime/temporal resolution to more accurately portray data trends;providing a longer window for calibration; increasing the probabilitythat a user can get trend data; and, providing more universaloptimization due to larger pool of data.

[0220] The present invention includes, but is not limited to, methods,microprocessors programmed to execute the methods, and monitoringsystems (comprising, for example, a sampling device, a sensing device,and one or more microprocessors programmed to control, for example, (i)a measurement cycle utilizing the sampling and sensing devices, and (ii)data gathering and data processing related to the methods of the presentinvention).

[0221] In one aspect of the present invention, one or moremicroprocessors employ an algorithm comprising one or more of the“rolling value” calculations described above. Typically, such one ormore microprocessors are components of an analyte monitoring system.

[0222] As is apparent to one of skill in the art, various modificationand variations of the above embodiments can be made without departingfrom the spirit and scope of this invention. Such modifications andvariations are within the scope of this invention.

[0223] B. Interpolation/Extrapolation

[0224] In another aspect of the present invention, interpolation and/orextrapolation are used to estimate unusable, missing or error-associatedanalyte-related signals. Such signals may be unusable for a variety ofreasons, typically where an error has been detected that places adetected analyte-related signal in question. Readings with suchassociated errors are typically “skipped.” In the interpolation aspect,one or more previous analyte-related signals and one or more subsequentanalyte-related signals are used to estimate an interveninganalyte-related signal. In the extrapolation aspect, two or moreprevious analyte-related signals are used to estimate a subsequentanalyte-related signal. Interpolation and extrapolation of values arealso employed in another aspect of the present invention that reducesthe incident of failed calibrations, described below. Exemplary methodsare described below with reference to the use of two collectionreservoirs into which the analyte-containing samples are extracted.However, one of ordinary skill in the art, following the guidance of thepresent specification, could adapt this method for use a single sensoror with more than two sensors used to determine analyte-related signals.

[0225] The interpolation and/or extrapolation methods of the presentinvention can be applied to single-sensor or multiple-sensor (i.e., twoor more sensors) analyte monitoring systems. The following examplesillustrate application of the methods of the present invention to atwo-sensor system; however, modification of the method for applicationto analyte monitoring systems with a different number of sensors iswithin the ability of one of ordinary skill in the art in view of theteachings of the present specification.

[0226] In one aspect of the present invention, interpolation and/orextrapolation are used to estimate a skipped reading in a series ofreadings. For example, when using the rolling value described above, ifan A or B reading is skipped (e.g., because of associated error), valuessurrounding (interpolation) or preceding (extrapolation) can be used toestimate the skipped value. The estimated value may then be combinedwith adjacent values to provide, for example, an average “A/B” readingor a summed “A/B” reading related to analyte amount or concentration.

[0227] Further, interpolation and extrapolation methods may be combined.For example, one missing reading may be provided by interpolation andthen that reading may be used to in the extrapolation of another missingreading.

[0228] The present invention includes the use of relationships betweenthe signals obtained from the sensors to perform interpolation and/orextrapolation of estimated values. For example, in a two sensor system aratio of signals obtained from a first sensor relative to a secondsensor may be employed in such interpolations and/or extrapolations toestimate values. Examples of methods of interpolation and/orextrapolation that can be used to provide estimated values are presentedbelow.

[0229] In one embodiment, interpolation and/or extrapolation are used ina method for reducing the incidence of failed calibration for an analytemonitoring system. Calibration of analyte monitoring systems is oftenperformed using a second, independent device. For example, in the caseof the GlucoWatch biographer a calibration point relative to bloodglucose concentration is provided by the user. The GlucoWatch biographeris put in place on the subject, allowed time to stabilize by performingseveral rounds of sampling and sensing, then the user uses anindependent glucose monitor (for example performing a finger stick andusing an optical detection system) to obtain a blood glucose value at acalibration time point. The calibration blood glucose value is enteredinto the GlucoWatch biographer by the user and the biographer associatesthe blood glucose value with its own sensor determinations of glucoseamount for the same time point.

[0230] A series of samples is extracted from the subject using asampling device. The extraction takes place alternately into a firstcollection reservoir and then into a second collection reservoir. Eachsample comprises the analyte. In this example, the sampling devicecomprises first and second collection reservoirs and a measurement cyclerefers to extracting into the first collection reservoir, extractinginto said second collection reservoir, and sensing analyte in eachcollection reservoir. The sensing device is used to obtain a signal fromeach sample that is-related to the analyte amount or concentration inthe subject, thus providing a series of signals. In this example thesensing device comprises a first sensor (A) and second sensor (B),wherein (1) the first sensor (A) is in operative contact with the firstcollection reservoir and the second sensor (B) is in operative contactwith the second collection reservoir. Also, two consecutive signalscomprise a measurement cycle, and each of the two consecutive signals ishalf-cycle signal.

[0231] A calibration method is performed to relate analyte amount orconcentration in the subject to signals obtained from the sensors, wherethe calibration method comprises:

[0232] (i) obtaining a valid first half-cycle signal S_(J), where ahalf-cycle signal S_(J+1), or an estimate thereof, and a half-cyclesignal S_(J+2), or an estimate thereof, are both used in the calibrationmethod so that the sensor signals correlate to the analyte amount orconcentration in the subject, wherein the calibration method alsoemploys an analyte calibration value that is independently determined;

[0233] (ii) providing the analyte calibration value; and

[0234] (iii) selecting a conditional statement selected from the groupconsisting of:

[0235] (a) if neither the second half-cycle signal S_(j+1) nor the thirdhalf-cycle signal S_(J+2) comprise errors, then S_(J+1) and S_(J+2) areused in the calibration method;

[0236] (b) if only the second half-cycle signal S_(J+1) comprises anerror, then an estimated signal S^(E) _(j+1) is obtained by determiningan interpolated value using signal S_(j) and S_(J+2), wherein theinterpolated value is S^(E) _(J+1), and S^(E) _(J+1) and S_(J+2) areused in the calibration method;

[0237] (c) if only the third half-cycle signal S_(j+2) comprises anerror, then an estimated signal S^(E) _(J+2) is obtained by determiningan extrapolated value using signal S_(J) and S_(J+1), wherein theextrapolated value is S^(E) _(j+2), and S_(j+1) and S^(E) _(j+2) areused in the calibration method; and

[0238] (d) if both the second half-cycle signal S_(j+1) and the thirdhalf-cycle signal S_(J+2) comprise errors, then return to (i) to obtaina new, valid half-cycle signal S_(j) from a later measurement half-cyclethan the measurement half-cycle from which the previous, valid S_(J)half-cycle signal was obtained.

[0239] This method can be applied to data obtained using the GlucoWatchbiographer. When used in the GlucoWatch biographer this method ofprocessing data reduces the incident of failed calibrations. Thereduction was achieved by allowing calibrations in the presence of a“skip error” (i.e., when a signal is skipped) for only one of the two10-minute sensor half-cycles at calibration. In this case, the sensorsignal during the skipped period is estimated by a interpolation if itis the first half-cycle or extrapolation if it is the second half-cycle.

[0240] In one embodiment of the present invention, the calibrationmethod begins when a non-skipped half-cycle has been successfullycompleted in order to attempt calibration. Therefore, a skippedhalf-cycle immediately before the expected opening of a “calibrationwindow” causes the window to not open, or be “suppressed”, until ahalf-cycle free of skip errors is completed. A calibration window refersto a period of time in which a user enters an independently determinedanalyte calibration value. In this embodiment of the invention, anun-skipped half-cycle is a gating requirement for the calibrationprocess to be initiated. However, the method allows that estimatedsignals may be provided by interpolation or extrapolation should they beneeded.

[0241] One benefit of the processing method is a decrease in abortedcalibrations. Experiments performed in support of the present inventionshowed a reduction of more than 50% in aborted calibrations (not due toout-of-range entry) when the methods described herein were employed.When out-of-range entries were considered, a 21% decrease in failedcalibrations was observed.

[0242] The processing method described herein for reducing the incidenceof failed calibrations used three half-cycles, S_(J), S_(j+1),S_(J+2) atcalibration. In the absence of skip errors, signals from S_(j+1) andS_(j+2) were used to complete calibration. If either S_(j+1) and S_(J+2)(but not both) had a skip error, then a method of interpolations orextrapolations was invoked to estimate the signal at the skippedhalf-cycle. If both S_(J+1) and S_(J+2) contained skip errors, then afailed calibration resulted.

[0243] The interpolation method was invoked when calibration half-cycleS_(j+1) had a skip error while S_(j) and S_(j+2) did not. Note thatS_(j) and S_(J+2) were from the same sensor (A or B), while S_(j+1) wasfrom another sensor. One interpolation method, shown below in Eqn. 7Athrough Eqn. 7D, simply assumes that the estimated S_(j+1) lies at apoint on the line between S_(J) and S_(J+2), whose distance is relatedto the time interval between the points, with a correction fordifferences between Sensors A and B using an “AB ratio.” In oneembodiment of the present invention, the same AB ratio is used forinterpolation and/or extrapolation regardless of the sensor source(i.e., A or B) of the signals being used to calculate the estimated(i.e., interpolated/extrapolated) signal value. In another embodiment ofthe present invention the form of the AB ratio used for interpolationand/or extrapolation depends on the sensor source (i.e., A or B) of thesignals being used to calculate the estimated (i.e.,interpolated/extrapolated) signal value. A further discussion of the ABratio is presented below. An exemplary interpolation method follows herefor a two sensor system where a different forms of the AB ratio are useddepending on the source of the signals being used in the calculation.

[0244] In the situation where both S_(j) and S_(j+2) are signals fromthe B sensor (S^(B) _(j) and S^(B) _(J+2)), and S_(J+1) is beingestimated for the A sensor signal (S^(AE) _(J+1)), interpolation Eqn. 7Amay be employed as follows: $\begin{matrix}{S_{j + 1}^{AE} = {\frac{A}{B}\left\{ {S_{j}^{B} + {\left( {S_{j + 2}^{B} - S_{j}^{B}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\quad \text{7A}}\end{matrix}$

[0245] wherein t is the time interval, for example, measurementhalf-cycle t_(J), one subsequent half-cycle, t_(J+1), or two subsequenthalf-cycles t_(J+2). When the points are equally spaced, that is when2(t_(j+1)-t_(j))=(t_(j+2)-t_(j)), then Eqn. 7A reduces to the followingEqn. 7B: $\begin{matrix}{S_{j + 1}^{AE} = {\frac{A}{B}\left( \frac{S_{j}^{B} + S_{j + 2}^{B}}{2} \right)}} & {{Eqn}.\quad \text{7B}}\end{matrix}$

[0246] In the situation where both S_(j) and S_(j+2) are signals fromthe A sensor (S^(A) _(j) and S^(A) _(j+2)), and S_(j+1) is beingestimated for the B sensor signal (S^(BE) _(J+1)), interpolation Eqn. 7Cmay be employed as follows: $\begin{matrix}{S_{j + 1}^{BE} = {\frac{B}{A}\left\{ {S_{j}^{A} + {\left( {S_{j + 2}^{A} - S_{j}^{A}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\quad \text{7C}}\end{matrix}$

[0247] When the points are equally spaced, that is when2(t_(j+1)-t_(J))=(t_(J+2)-t_(J)), then Eqn. 7C reduces to the followingEqn. 7D: $\begin{matrix}{S_{j + 1}^{BE} = {\frac{B}{A}\left( \frac{S_{j}^{A} + S_{j + 2}^{A}}{2} \right)}} & {{Eqn}.\quad \text{7D}}\end{matrix}$

[0248] The extrapolation method was invoked when calibration half-cycleS_(J+2) had a skip error while S_(j) and S_(J+1) did not. Note thatS_(j) and S_(j+1) are from different sensors (A and B), while S_(j+2) isfrom the same sensor as S_(j). The extrapolation method, shown in theEqn. 8A through Eqn. 8D, assumes the extrapolated point is on a lineconnecting S_(j) and S_(j+1), using a correction for differences betweensensors A and B, and estimates a value for S_(j+2). As noted above, asingle AB ratio may be employed or the AB ratio may take different formsdepending on the sensor source of the signals. Further discussion of theAB ratio is presented below. An exemplary interpolation method followshere for a two-sensor system where different forms of the AB ratio areused depending on the source of the signals being used in thecalculation.

[0249] In the situation where S_(J) is signal from sensor A (S^(A) _(j))and S_(j+1) is signal from B sensor (S^(B) _(j+1)), and S_(j+2) is beingestimated for the A sensor signal (S^(AE) _(J+2)), extrapolation Eqn. 8Amay be employed as follows: $\begin{matrix}{S_{j + 2}^{AE} = {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} + \left\lbrack {\left\{ {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} - S_{j}^{A}} \right\} \frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\quad \text{8A}}\end{matrix}$

[0250] When the points are equally spaced, that is when(t_(J+2)-t_(J+1))=(t_(j+1)-t_(j)), then Eqn. 8A reduces to the followingEqn. 8B: $\begin{matrix}{S_{j + 2}^{AE} = {{2\frac{A}{B}S_{j + 1}^{B}} - S_{j}^{A}}} & {{Eqn}.\quad \text{8B}}\end{matrix}$

[0251] In the situation where S_(j) is signal from the B sensor (S^(B)_(J)) and S_(J+1) is signal from the A sensor (S^(A) _(j+1)), andS_(j+2) is being estimated for the B sensor signal (S^(BE) _(J+2)),extrapolation Eqn. 8C may be employed as follows: $\begin{matrix}{S_{j + 2}^{BE} = {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} + \left\lbrack {\left\{ {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} - S_{j}^{B}} \right\} \frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\quad \text{8C}}\end{matrix}$

[0252] When the points are equally spaced, that is when(t_(j+2)-t_(j+1))=(t_(j+1)-t_(j)), then Eqn. 8C reduces to the followingEqn. 8D: $\begin{matrix}{S_{j + 2}^{BE} = {{2\frac{B}{A}S_{j + 1}^{A}} - S_{j}^{B}}} & {{Eqn}.\quad \text{8D}}\end{matrix}$

[0253] The interpolation and extrapolation methods described above use arelationship between the signals of sensor A and B. One method ofdetermining this relationship is to calculate a weighted average using asmoothing protocol. Smoothing methods that may be employed include, butare not limited to, (i) taking a basic average of all available ratios,(ii) initializing the processing method with the ratio at some specificpoint, (iii) trend methods, and (iv) methods including exponentialcomponents. One exemplary smoothing protocol useful in the practice ofthe present invention is the Holt-Winters smoothing (examplescorresponding to the ratios used above are shown in Eqn. 9A and Eqn.9B). $\begin{matrix}{\left( \frac{A}{B} \right)_{s,i} = {{w\left( \frac{A}{B} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{A}{B} \right)_{s,{i - 1}}}}} & {{Eqn}.\quad \text{9A}} \\{\left( \frac{B}{A} \right)_{s,i} = {{w\left( \frac{B}{A} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{B}{A} \right)_{s,{i - 1}}}}} & {{Eqn}.\quad \text{9B}}\end{matrix}$

[0254] In Eqn. 9A and Eqn. 9B, (A/B)_(s,i) and (B/A)_(S,l) refer to“smoothed” AB ratios for measurement cycle i, (A/B)₁ and (B/A)₁ refer tothe AB ratio for measurement cycle i, and (A/B)_(s,i−1) and(B/A)_(s,l−1), refer to the smoothed AB ratio from the previousmeasurement cycle i−1. In the Holt-Winters smoothing presented above,the determination of the smoothed AB ratio depends on the adjustableparameter w (a weighting factor). Experiments performed in support ofthe present invention were carried out on the sensor signals at earlytimes for the GlucoWatch biographer. Predictions of sensor signal weregenerated at all potential points with both interpolation andextrapolation. For those points where an actual sensor signal wasavailable, a relative error was calculated. The mean relative absoluterelative error (MARE) was found for each sensor for each method at eachsmoothing weight. Based on an initial analysis a smoothing weight 0.7(i.e., 70%) was chosen. Other smoothing weights may be employed where wis a smoothing factor and represents a numerical, percentage valuebetween and inclusive of 0 to 100%, where w is represented by a fractionbetween and inclusive of 0 to 1.

[0255] Experiments performed in support of the present invention suggestthat a reduction of failed calibrations is the main impact of the newprocessing method, thus resulting in better “usability” of theGlucoWatch biographer, in the sense that the number of finger-pricksrequired for calibration (i.e., the analyte calibration valuedindependently obtained) is greatly reduced.

[0256] The smoothed AB ratios described above were used in theinterpolation and extrapolation of skipped half-cycle signals, e.g., atcalibration. Smoothing is not essential and in some applications a fixedAB ratio may be employed. It was initially assumed that the smoothed B/Aratio would be reciprocal of the smoothed A/B ratio. When this is thecase a single AB ratio may be used. However, experiments performed insupport of the present invention that employed the GlucoWatch biographerindicated that the smoothed B/A ratio was not mathematically equivalentto the reciprocal of the smooth A/B ratio and that in some applicationsuse of separate smoothed A/B and B/A ratios (as shown above) providesmore reliable results for the interpolation and extrapolation of bothsensors, e.g., at calibration.

[0257] The processing methods discussed above are optimal when both A/Band B/A ratios are calculated separately for each consecutive non-skip Bcathode and A cathode half-cycles. As new A/B and B/A ratios arecalculated for news cycles, the most recent one is smoothed with theprevious one. One exemplary smoothing method is performed according tothe-following general equation (Eqn. 10):

R _(l) ^(s) =wR _(l)+(1−w)R _(l−1) ^(s)   Eqn. 10

[0258] wherein, R_(i) is the A/B or B/A ratio for a i^(th) measurementcycle, R^(S) _(i) is smoothed R for a i^(th) measurement cycle, and w isa smoothing factor and represents a numerical, percentage value betweenand inclusive of 0 through 100%, where w is represented by a fractionbetween and inclusive of 0 through 1, and R^(S) _(l−1) is a smoothedratio for the (i−1)^(th) measurement cycle, wherein the i^(th)measurement cycle is composed of first and second half-cycles and thesecond half-cycle value of the i^(th) measurement cycle precedes S_(J).A single R^(S) ₁ may be used or more than one such ratio may beemployed.

[0259] The smoothed ratios of A/B and B/A are stored and used for aninterpolation and extrapolation of skipped cycle intervals, e.g., duringcalibrations.

[0260] In order to compare interpolation and/or extrapolation estimatesusing a single AB ratio versus methods employing separate A/B and B/Aratios, a calibration cycle with interpolation and extrapolation wassimulated without specifying the identity of the A and B sensors. Theneach sensor was arbitrarily assigned to A or B. When a single AB ratiowas maintained (single AB ratio method), the interpolation anextrapolation results are dependent on which sensor was assigned A andwhich sensor was assigned B. When separate A/B and B/A ratios weremaintained (separate A/B and B/A ratio method), the interpolation andextrapolation results were identical when the A and B sensor assignmentswere switched. This simulation suggested that calculating separate A/Band B/A ratios was more accurate than using a single AB ratio. However,using a single AB ratio still provides an efficacious means forcalculating estimated values.

[0261] Experiments performed in support of the present invention suggestthat minor inconsistencies in interpolation and extrapolationcalculations that resulted from the use of a single smooth AB ratio forboth the A and B sensors was eliminated when separate smoothed A/B andB/A ratios are maintained.

[0262] In this modification of methods of making interpolation and/orextrapolation estimates of the present invention, the A/B ratio and B/Aratio can, for example, store ratios on the A and B signal integralsfrom the later part of the equilibration period. They are then used asneeded interpolation and/or extrapolation of skipped signals.

[0263] In the context of the methods of the present invention forreducing the number of failed calibrations applied to the GlucoWatchbiographer, an acceptable A/B (and B/A) ratio is generated if there is aleast one pair of consecutive non-skipped A and B signals prior to thisfirst good half-cycle signal that begins the calibration. Until good A/Band B/A ratios are available, a biographer employing the methods of thepresent invention does not open a calibration window and calibrationwill not be performed.

[0264] If more than one pair is available, then a smoothing techniquemay be used to obtain a rolling value (for example, as shown in Eqn. 10,above):

R _(l) ^(s) =wR _(l)+(1−w)R _(l−1) ^(s)   Eqn. 10

[0265] where R is the A/B ratio or B/A ratio, the smoothing factor wrepresents a numerical, percentage value between and inclusive of 0 to100%, where w is represented by a fraction between and inclusive of 0to 1. To maximize consistency with signals from the A and B sensors andfor calculation of skipped integrals, separate A/B and B/A ratios aremaintained for use in the ratio smoothing equation. The AB and BA ratiosmay be maintained after calibration for use, for example, duringrecalibration or for interpolation and/or extrapolation of later missingvalues. The ratios may be updated after each pair of consecutivenon-skipped A and B signals.

[0266] As discussed above, the interpolation method is invoked whencalibration half-cycle S_(j+1) has a skip error while S_(j) and S_(J+2)do not. Note that S_(j) and S_(J+2) are from the same sensor (A or B),while S_(j+1) is from the other sensor. The interpolation method, shownabove, assumes that S_(j+1) lies at the vertical midpoint between S_(J)and S_(j+2), and is then corrected for Sensor A to B differences by asfollows: when interpolating for the A sensor (S_(J+1) is the A sensor),the A/B ratio is used. When interpolating for the B sensor (S_(J+1) isthe B sensor), the B/A ratio is used.

[0267] Also as discussed above, the extrapolation method is invoked whencalibration half-cycle S_(j+2) has a skip error while S_(J) and S_(j+1)do not. Note that S_(J) and S_(j+1) are from different sensors (A andB), while S_(j+2) is from the same sensor as S_(j). The extrapolationmethod, shown above, makes essentially the same assumptions as theinterpolation method, but solves for S_(J+2). When extrapolating for theA sensor the Ratio_(ab) factor is equal to the A/B ratio. Whenextrapolating for B sensor the Ratio_(ab) factor is equal to the B/Aratio.

[0268] The present invention includes, but is not limited to, methods,microprocessors programmed to execute the methods, and monitoringsystems (comprising, for example, a sampling device, a sensing device,and one or more microprocessors programmed to control, for example, (i)a measurement cycle utilizing the sampling and sensing devices, and (ii)data gathering and data processing related to the methods of the presentinvention).

[0269] Further, experiments performed in support of the presentinvention that utilize the GlucoWatch biographer have indicated thatskipped signals tend to occur in clusters. Accordingly, one aspect ofthe present invention comprises waiting for an unskipped (i.e., errorfree or good signal) half-cycle signal before initiating a calibrationsequence (e.g., before opening a calibration window inviting the user toprovide an independently determined analyte calibration value). Such anindependently determined analyte calibration value may be obtained, forexample, by using a traditional blood glucose measuring device, e.g.,blood glucose amounts as determined using a OneTouch® (Johnson &Johnson, New Brunswick, N.J.) blood glucose monitoring device.

[0270] Although the above-methods have been exemplified for two sensors,the methods can be applied to single-sensor or multiple-sensor (i.e.,two or more sensors) devices by one of ordinary skill in the art in viewof the teachings of the present specification.

[0271] The present invention includes, but is not limited to, methods,microprocessors programmed to execute the methods, and monitoringsystems (comprising, for example, a sampling device, a sensing device,and one or more microprocessors programmed to control, for example, (i)a measurement cycle utilizing the sampling and sensing devices, and (ii)data gathering and data processing related to the methods of the presentinvention).

[0272] In one aspect of the present invention, one or moremicroprocessors employ an algorithm comprising one or more of theinterpolation, extrapolation, and/or A/B ratio calculations describedabove. Typically, such one or more microprocessors are components of ananalyte monitoring system.

[0273] As is apparent to one of skill in the art, various modificationand variations of the above embodiments can be made without departingfrom the spirit and scope of this invention. Such modifications andvariations are within the scope of this invention.

[0274] C. Integral Splitting

[0275] One extension of using “rolling values” to increase frequency ofreadings is to use subsets of each trapezoidal integral (i.e., integralsplitting) to update the signal after each biosensor current reading istaken. For example, when each analyte-related signal is represented byan integral over time, rolling analyte measurement values may beobtained by integral splitting. In this way, the readings are reportedto the user as new information is obtained. This allows readings to begiven as often as current readings can be taken, which may be, forexample, as often as more than once per second. FIGS. 3 and 4 illustratethe concept of integral splitting. In FIGS. 3A to 3C, three differentread frequencies schemes are shown that range from serial pairedmeasurements (AB, AB, AB; FIG. 3A), to a “rolling value” measurement(AB, BA, AB, BA; FIG. 3B), and finally an “integral split” measurement(FIG. 3C), where readings are provided most frequently. In thisnomenclature, the numbers in parentheses are “ranges” of trapezoidalsegments used for each measurement (e.g. A2(2-N)A3(1)B2 means to use theentire B2 integral, along with the first segment of the A3 and fromsegment 2 to N of A2 with N being the total number of segments)—theseexpressions are not mathematical formulae.

[0276] In FIG. 4A, an example is shown of how the newest trapezoidalsegment replaces the oldest one as a new current value is taken. In thisexample, the eighth sensor reading completes the seventh trapezoidalsegment of Integral A3. This segment A3(7) replaces A2(7), so that thefinal signal used for calculation changes from A2(7-N)A3(1-6)B2 toA2(8-N)A3(1-7)B2. FIG. 4B is an illustration of the increase in readingfrequency for various measurement methods described above (e.g.,integral splitting and rolling values relative to serial paired).

[0277] The present invention includes, but is not limited to, methods,microprocessors programmed to execute the methods, and monitoringsystems (comprising, for example, a sampling device, a sensing device,and one or more microprocessors programmed to control, for example, (i)a measurement cycle utilizing the sampling and sensing devices, and (ii)data gathering and data processing related to the methods of the presentinvention).

[0278] In one aspect of the present invention, one or moremicroprocessors employ an algorithm comprising integral splittingcalculations as described above to provide readings as often as currentreadings can be taken. Typically, such one or more microprocessors arecomponents of an analyte monitoring system.

[0279] As is apparent to one of skill in the art, various modificationand variations of the above embodiments can be made without departingfrom the spirit and scope of this invention. Such modifications andvariations are within the scope of this invention.

[0280] D. Recalibration Methods

[0281] (i) Optional Recalibrations

[0282] The following recalibration methods are described with referenceto the GlucoWatch biographer, however, in view of the teachings of thepresent specification one of ordinary skill in the art can apply theserecalibration methods to any analyte monitoring system that provides aseries of analyte readings dependent on obtaining a calibration value.As described above, the GlucoWatch biographer is an exemplary analytemonitoring system where the analyte of interest is glucose. In theGlucoWatch biographer, a user is able to recalibrate the GlucoWatchbiographer at any time if, for example, the GlucoWatch biographerreadings are inconsistent with the user's physical symptoms or if theuser receives a number of SKIP messages (i.e., messages relating to datathat do not conform to predetermined criteria (for example,error-associated criteria as described in U.S. Pat. No. 6,233,471,herein incorporated by reference). In the GlucoWatch biographer, if theuser decides to recalibrate the GlucoWatch biographer and isunsuccessful for any reason (for example, due to SKIP errors orout-of-range entry), the previously entered calibration is terminatedand the GlucoWatch biographer reverts to an uncalibrated state. TheGlucoWatch biographer is then only able to generate glucose readings ifthe re-calibration entry is successful. This situation can potentiallylead to long periods of time in which the user receives no glucosereadings. This situation is illustrated in FIG. 5.

[0283] A useful modification of the above-described recalibrationfeature is that during a failed optional recalibration, the glucosemonitoring system can continue to generate glucose readings using thepreviously accepted calibration value while the glucose monitoringsystem assesses the entered re-calibration value. Once the entered,re-calibration value is accepted, the glucose monitoring system beginsto generate glucose readings using the new calibration value. Thissituation is illustrated in FIG. 6.

[0284] (ii) Consecutive Skipped Measurements and Required Re-Calibration

[0285] The following recalibration methods are described with referenceto the GlucoWatch biographer, however, in view of the teachings of thepresent specification one of ordinary skill in the art can apply theserecalibration methods to any analyte monitoring system that provides aseries of analyte readings dependent on obtaining a calibration value.If the GlucoWatch biographer produces six consecutive skipped readings,then the GlucoWatch biographer aborts the sequence and the user mustchange the AutoSensor and perform a warm-up and calibration periodbefore the GlucoWatch biographer will produce glucose measurementsagain. The sequence is aborted and the measurement period is terminatedearly, for example, to safeguard against potentially inaccuratereadings. When the sequence aborts, the GlucoWatch biographer sounds adistinctive beep, and an error message is displayed. Additional researchhas shown that the number of consecutive skips may be increased from sixto eighteen without reducing the safety or effectiveness of the device.

[0286] In addition to increasing the number of allowable consecutiveskips, rather than aborting the sequence, the user may be asked tore-calibrate the device after, for example, eighteen consecutive skips.After eighteen consecutive skips, the analyte monitoring system removesthe original calibrations value and requests a new conventional meterblood glucose measurement. This is termed “Required Re-Calibration. ”During Required Re-Calibration, the user engages the same calibrationprocess that is normally performed at the end of a warm-up period. Forthis Re-Calibration method, the calibration integrity checks(interpolation and/or extrapolation, suppression) are applied tore-calibrating the analyte monitoring device.

[0287] Accordingly, for an analyte monitoring system that requirescalibration, a number of consecutive skips can be determined that stillallows the analyte monitoring system to provide safe and effectivereadings. After the number of consecutive skips is met or exceeded oneor more microprocessors of the analyte monitoring system may beprogrammed to force a required recalibration.

[0288] The present invention includes, but is not limited to, methods,microprocessors programmed to execute the methods, and monitoringsystems (comprising, for example, a sampling device, a sensing device,and one or more microprocessors programmed to control, for example, (i)a measurement cycle utilizing the sampling and sensing devices, and (ii)data gathering and data processing related to the methods of the presentinvention).

[0289] In one aspect of the present invention, one or moremicroprocessors employ an algorithm comprising programmed instructionsto execute the above described recalibration methods. Typically, suchone or more microprocessors are components of an analyte monitoringsystem.

[0290] As is apparent to one of skill in the art, various modificationand variations of the above embodiments can be made without departingfrom the spirit and scope of this invention. Such modifications andvariations are within the scope of this invention.

[0291] 6. Methods of Providing an Analyte Concentration-Related Alert

[0292] Following here two approaches are described to provide an analyteconcentration-related alert when an analyte level falls above or belowpredetermined thresholds or outside of a predetermined range ofreference values: gradient methods and predictive algorithm methods.These methods provide for predicting an analyte concentration-relatedevent in a subject being monitored for levels of a selected analyte.Exemplary analyte concentration-related alerts include, but are notlimited to, a “down alert,” that is an alert when an analyte levelsfalls below a predetermined value or range of values (e.g., ahypoglycemic alert), and an “up alert,” that is, an alert is providedwhen an analyte level falls above a predetermined value or range ofvalues (e.g., a hyperglycemic alert).

[0293] The gradient method employs the current and the past analytemonitoring system reading and determines the rate of decline and/orincrease. The rate of change of the analyte (e.g., a rate of change ofthe analyte in the direction of decreasing amount of analyte) in thesubject is then used to determine whether to alert the subject or not.One limitation of this method in the context of glucose monitoring isthat, when this method is used alone for the prediction of, e.g., ahypoglycemic event, it would trigger the down alert even at very highblood glucose levels when the rate of decline exceeded the acceptable,predetermined rate of change.

[0294] The predictive algorithm method uses a predictive algorithm topredict the next analyte reading based on previously obtained analytereadings (e.g., obtained using the GlucoWatch biographer). Based on thevalue of this predicted reading relative to predetermined thresholdvalues or range of values, the analyte concentration-related alert wouldor would not be triggered. The gradient method and the predictivealgorithm method may be combined. One preferred embodiment is discussedbelow where the gradient method is combined with the predictivealgorithm method. Further, such a combination method may be combinedwith individual predictors (such as, skin conductivity and temperature).

[0295] A. The Gradient Method

[0296] Several models for the determination of a gradient (i.e., therate of change) are as follows: $\begin{matrix}{{{{Model}\quad A\text{:}\quad \frac{y_{(n)} - y_{({n - 1})}}{\Delta \quad t}\quad \left( {{concentration}/{time}} \right)};{{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 1})}} \right)}}\quad} \\{{{Model}\quad B\text{:}\quad \frac{y_{(n)} - y_{({n - 1})}}{y_{({n - 1})}\Delta \quad t}\quad \left( {{fractional}\quad {{change}/{time}}} \right)};{{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 1})}} \right)}} \\{{{Model}\quad C\text{:}\quad \frac{y_{(n)} - y_{({n - 2})}}{\Delta \quad t}\quad \left( {{concentration}/{time}} \right)};{{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 2})}} \right)}} \\{{{Model}\quad D\text{:}\quad \frac{y_{(n)} - y_{({n - 2})}}{y_{({n - 2})}\Delta \quad t}\quad \left( {{fractional}\quad {{change}/{time}}} \right)};{{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 2})}} \right)}} \\{{{Model}\quad E\text{:}\quad {{Average}\left\lbrack {\frac{y_{(n)} - y_{({n - 1})}}{\Delta \quad t_{1}},\frac{y_{({n - 1})} - y_{({n - 2})}}{\Delta \quad t_{2}},\frac{y_{(n)} - y_{({n - 2})}}{\Delta \quad t_{3}}} \right\rbrack}}\quad} \\{\quad {\left( {{concentration}/{time}} \right);}}\end{matrix}$

[0297] where

[0298] Δt₁=(t_((n))−t_((n−1))), Δt₂=(t_((n−1))−t_((n−2))), andΔt₃=(t_((n))-t_((n−2))). In this model, the average is of all threevalues shown in the brackets.${{Model}\quad F\text{:}\quad \frac{y_{(n)} - y_{({n - 3})}}{y_{({n - 3})}\Delta \quad t}\quad \left( {{fractional}\quad {{change}/{time}}} \right)};{{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 3})}} \right)}$

[0299] In the above models, y_(n) stands for an analyte reading at timepoint t_((n)), y_((n−1)) an analyte reading at time point t_((n−1))(i.e., the previous reading to y_(n)), y_((n−2)) an analyte reading attime point t_((n−2)) (i.e., the reading previous to y_((n−1))),y_((n−3)) an analyte reading at time point t_((n−3)) (i.e., the readingprevious to Y_((n−2))). Each of the above methods give a rate of change.Models A, C, and E give concentration change per time interval, forexample, the units may be mg/dL/minute or mmol/L/minute when y is aglucose reading. Models B, D, and F give a fractional change per timeinterval (e.g., percentage change in the glucose reading per minute).When using a gradient method a threshold of an acceptable rate of changeis selected (for example, based on experimental data and/or acceptableranges of measurement values).

[0300] In one embodiment, a microprocessor employs an algorithmcomprising the selected model and calculates the rate of change (e.g.,in the indicated units). The microprocessor then employs an algorithm tocompare the calculated rate of change to a predetermined acceptable rateof change. If the calculated rate of change differs significantly fromthe acceptable rate of change then the microprocessor triggers theanalyte monitoring system to provide an alert to the user. For example,applying Model D to glucose readings in order to predict a hypoglycemicevent an acceptable rate of change may be established as a decrease of1.75%/min for Δt=20 min. If the rate of change exceeds a decrease of1.75%/min for Δt=20 min then the subject is alerted to this fact (e.g.,by an audible alert and/or a prompt on the user interface).

[0301] Typically when employing the gradient models, to provide alow-analyte alert (e.g., hypoglycemic event alert) the calculated rateof change is negative and less than the predetermined threshold rate ofchange (e.g., a calculated rate of change of negative 2%/min for Δt=20minutes is less than the threshold value of negative change of 1.75%/minfor Δt=20 min); and/or to provide a high-analyte alert (e.g.,hyperglycemic event alert) the calculated rate of change is positive andgreater than the predetermined threshold rate of change (e.g., acalculated rate of change of 2%/min for Δt=20 minutes is greater thanthe threshold value of change of 1.75 %/min for Δt=20 min).Alternatively, absolute values of the calculated and threshold rates ofchange may be used for comparison. In this case, an alert is providedwhen the absolute value of the calculated rate of change is greater thanthe absolute value of predetermined threshold rate of change.

[0302] In addition to the above-described gradient models, a number ofother models can be employed in a gradient method, including, but notlimited to, use of a regression model to determine the gradient, using,for example, a best-fit function.

[0303] B. The Predictive Algorithm Method

[0304] One predictive algorithm method (Eqn. 11) has been previouslydescribed for use in time-series predictions (U.S. Pat. No. 6,272,364,herein incorporated by reference in its entirety). Several otherpredictive algorithm methods follow here as well. $\begin{matrix}{y_{({n + 1})} = {y_{(n)} + {\alpha \left( {y_{(n)} - y_{({n - 1})}} \right)} + {\frac{\alpha^{2}}{2}\left( {y_{(n)} - {2y_{({n - 1})}} + y_{({n - 2})}} \right)}}} & {{Eqn}.\quad 11} \\{y_{({n + 1})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 1})} - t_{n}} \right)}}} & {{Eqn}.\quad 12} \\{y_{({n + 1})} = {{\frac{5}{2}y_{(n)}} + {{- 2}\left( y_{({n - 1})} \right)} + {\frac{1}{2}\left( y_{({n - 2})} \right)}}} & {{Eqn}.\quad 13} \\{y_{({n + 2})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 2})}} \right)}{\left( {t_{n} - t_{({n - 2})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} & {{Eqn}.\quad 14} \\{y_{({n + 2})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} & {{Eqn}.\quad 15}\end{matrix}$

[0305] In these equations, the methods calculate the predicted value ofa variable y (e.g., concentration of analyte) at time t_(n+1) (ort_(n+2), as indicated) as a function of that variable at the currenttime t_(n), as well as at a previous time or times, e.g., t_(n−1) and/ort_(n−2)). In these equations, Y_((n+1)) and Y_((n+2)) are predictedvalues of variable y at time points (n+1) and (n+2), respectively,Y_((n)), Y_((n−1)), Y_((n−2)) are analyte-related values at times (n),(n−1), and (n−2), respectively, t_((n−2)), t_((n−1)), t_((n)),t_((n+1)), t(n+₂), are time points at times (n−2), (n−1), (n), (n+1) and(n+2), respectively. In Eqn. 11, α is an empirically determinedweighting value that is typically a real number between 0 and 1. Each ofthe above methods provides a predicted analyte value, for example, anamount or concentration (e.g., the units may be mg/dL (milligrams ofglucose per deciliter) or mmol/L when y is a glucose reading). Whenusing a predictive algorithm thresholds of an acceptable range foranalyte amount or concentration are selected (for example, based onexperimental data and/or acceptable ranges of measurement values). Highthreshold values may be selected (e.g., a glucose value that isconsidered hyperglycemic for a subject), low threshold values may beselected (e.g., a glucose value that is considered hypoglycemic for asubject), and/or an acceptable range of values with an associated errormay also be employed.

[0306] In one embodiment, one or more microprocessors employ analgorithm comprising the selected predictive algorithm and calculatesthe predicted value (e.g., in the indicated units). The microprocessorthen employs an algorithm to compare the predicted value to thethreshold value(s). If the predicted value falls above a high threshold,below a low threshold, or outside of a predetermined range of values,then the microprocessor triggers the analyte monitoring system toprovide an alert to the user.

[0307] When the analyte being monitored is glucose and glucose readingsare provided by a glucose monitoring device (e.g., the GlucoWatchbiographer) y_(n) corresponds to GW_(n), a glucose value in the subjectat time t_(n). Further, for prediction of glucose values when using Eqn.11, α typically equals 0.5.

[0308] Eqn. 11 predicts the next analyte value (y_((n+1))) based on thecurrent analyte value (y_(n)), and two previous analyte values y_((n−1))and y_((n−2)), wherein the weight of the effect of y_((n−2)) isdetermined by the weighting factor α. Eqn. 12 predicts the next analytevalue (y_((n+1))) based on the current analyte value (y_(n)), a previousanalyte value y_((n−1)), and time intervals associated with the times atwhich the analyte values are determined (for (y_(n)) and y_((n−1))) orpredicted (y_((n−1))). Eqn. 13 predicts the next analyte value(y_((n+1))) based on the current analyte value (y_(n)), and two previousanalyte values Y_((n−1)) and y_((n−2)). Eqn. 14 predicts an analytevalue(y_((n+2))) at two time points after t_(n) based on the currentanalyte value (y_(n)), a previous analyte value y_((n−2),) and timeintervals associated with the times at which the analyte values aredetermined (for (y_(n)) and Y_((n−2))) or predicted (Y_((n+2))). Eqn. 15predicts an analyte value(y_((n+2))) at two time points after t_(n)based on the current analyte value (y_(n)), a previous analyte valuey_((n−1)), and time intervals associated with the times at which theanalyte values are determined (for (Y_(n)) and y_((n−1))) and predicted(Y_((n+2))).

[0309] As noted above, when employing the above predictive algorithms,an alert/alarm can be used to notify the subject (or user) if thepredicted value is above/below a predetermined threshold. For example,in the situation where glucose is the analyte being monitored, a lowthreshold of greater than 80 mg/dL may be selected for a particularsubject. Accordingly predicted glucose values (obtained by using any ofthe above predictive algorithms) of 80 or less may trigger an alert tothe subject. Typically low threshold values for glucose are betweenabout 50-100 mg glucose per dL blood and high threshold values arebetween about 200-300 mg glucose per dL blood.

[0310] C. Combined Approach

[0311] In one embodiment of the present invention to provide an analyteconcentration-related alert when an analyte level falls above or belowpredetermined thresholds or outside of a predetermined range ofreference values, an approach combining the above-described gradientmethod and predictive algorithm method is employed. In this embodimentof the present invention, rate of change thresholds are determined aswell as analyte thresholds (or range of values). Generally, a predictivealgorithm is chosen which provides a predicted analyte value at a futuretime point. The predicted value is compared to the threshold value forthe alert. If the predicted value exceeds the threshold value, then therate of change of the analyte is evaluated. If the rate of change of theanalyte level surpasses a predetermined threshold (or falls outside of arange of values) then an analyte concentration-related alert is providedto the subject in whom the analyte levels are being monitored. Of coursethe order of these two comparisons (i.e., predicted value and rate ofchange) may be reversed, for example, where the rate of change isevaluated first and then the predicted value is evaluated.

[0312] For example, in the context of providing an alert for a futurehypoglycemic event, a “low alert threshold” is selected. Such athreshold is typically user selected and falls in the range of about 50mg/dL (glucose/blood volume) to about 100 mg/dL. Additionally, one ormore microprocessors of the monitoring system may be programmed tomodify the user the selected threshold by, for example, adding apredetermined value to the selected threshold (for example, if athreshold of 80 mg/dL is selected a program of the monitoring system mayadd 10 mg/dL to the threshold, resulting in a low alert threshold of 90mg/dL).

[0313] A predictive algorithm is chosen (e.g., based on Eqn. 15, above)$\begin{matrix}{\left. {{GW}_{({n + 2})} = {{GW}_{(n)} + {\frac{\left( {{GW}_{(n)} - {GW}_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} \right).} & {{Eqn}.\quad 16}\end{matrix}$

[0314] When the current glucose value, e.g., as determined by theGlucoWatch biographer, is equal to or less than a predetermined valuethe predictive algorithm is invoked to predict a glucose value at afuture time point (for Eqn. 16 that would be n+2). Simultaneously, orsequentially, a gradient method is employed to determine the rate ofchange of the glucose values, for example, using Model B above where yequals the glucose value as determined by the GlucoWatch biographer, andthe threshold fractional change/time (e.g., %/min) is defined, forexample, as negative 5%/10 minutes:${Model}\quad B^{\prime}\quad {\frac{{GW}_{(n)} - {GW}_{({n - 1})}}{{GW}_{({n - 1})}\Delta \quad t}.}$

[0315] The rate of change as determined by the gradient method iscompared to a threshold value or range of threshold values. If theglucose value predicted by the predictive algorithm is less than orequal to the predetermined low threshold value, and the rate of changeis negative and less than the predetermined threshold rate of changethen an alert is provided to the subject in anticipation of ahypoglycemic event.

[0316] The present invention includes, but is not limited to, methods,microprocessors programmed to execute the methods, and monitoringsystems (comprising, for example, a sampling device, a sensing device,and one or more microprocessors programmed to control, for example, (i)a measurement cycle utilizing the sampling and sensing devices, and (ii)data gathering and data processing related to the methods of the presentinvention).

[0317] In one aspect of the present invention, one or moremicroprocessors employ an algorithm comprising programmed instructionsto execute the above described combined methods for providing ananalyte-concentration related alert. Typically, such one or moremicroprocessors are components of an analyte monitoring system.

[0318] In one aspect of the present invention, the rolling valuesdescribed above are employed as the measurement data points in the“analyte concentration-related” alert methods. As noted above, therolling value method of the present invention provides for more frequentupdating and reporting of analyte measurement values. In a furtheraspect of the present invention interpolation and/or extrapolationmethods are employed to provide missing or error-associated signals inthe series of analyte-related signals. As discussed above, one or moremicroprocessors may be programmed to execute the calculations associatedwith a rolling value method and/or an analyte-concentration relatedalert method.

[0319] As is apparent to one of skill in the art, various modificationand variations of the above embodiments can be made without departingfrom the spirit and scope of this invention. Such modifications andvariations are within the scope of this invention.

What is claimed is:
 1. A method of increasing the number of analytemeasurement values related to the amount or concentration of an analytein a subject as measured using an analyte monitoring device, said methodcomprising providing a series of analyte-related signals obtained fromthe analyte monitoring device over time, wherein (i) two or morecontiguous analyte-related signals are used to obtain a single analytemeasurement value (M), (ii) analyte-related signals are not used tocalculate more than one analyte measurement value, and (iii) said two ormore contiguous analyte-related signals, used to obtain the singleanalyte measurement value, comprise first and last analyte-relatedsignals of the series; mathematically computing rolling analytemeasurement values, wherein (i) each rolling analyte measurement valueis calculated based on two or more contiguous analyte-related signalsfrom the series of analyte-related signals obtained from the analytemonitoring device, (ii) a subsequent rolling analyte measurement valueis mathematically computed by dropping said first analyte-related signaland including an analyte-related signal contiguous and subsequent to thelast analyte-related signal, (iii) further rolling analyte measurementvalues are obtained by repeating the dropping of the firstanalyte-related signal used to calculate the previous rolling analytemeasurement and including an analyte-related signal contiguous andsubsequent to the last analyte-related signal used to calculate theprevious rolling analyte measurement, and (iv) each rolling analytemeasurement value provides a measurement related to the amount orconcentration of analyte in the subject; and increasing the number ofanalyte measurement values derived from the analyte-related signals inthe series of analyte-related signals obtained from the analytemonitoring device by serially calculating rolling analyte measurementvalues, thereby increasing the number of analyte measurement values. 2.The method of claim 1, wherein said rolling analyte measurement value isan average of two or more analyte-related signals.
 3. The method ofclaim 1, wherein said rolling analyte measurement value is a sum of twoor more analyte-related signals.
 4. The method of claim 1, wherein eachanalyte-related signal is represented by an integral over time, and saidrolling analyte measurement value is obtained by integral splitting. 5.The method of claim 1, wherein said monitoring device comprises asampling device and a sensing device, and wherein said providing theseries of analyte-related signals obtained from an analyte monitoringdevice comprises extracting a sample from the subject alternately into afirst collection reservoir and then into a second collection reservoirusing the sampling device, wherein (i) each sample comprises theanalyte, and (ii) said sampling device comprises said first and secondcollection reservoirs; and sensing the analyte in each extracted sampleto obtain a signal from each sample that is related to the analyteamount or concentration in the subject, thus providing a series ofanalyte-related signals, said sensing device comprising first and secondsensors, wherein said first sensor is in operative contact with saidfirst collection reservoir and said sensing provides signal S^(A) _(J)(where S^(A) is the signal from sensor A, j is the time interval), thesecond sensor is in operative contact with the second collectionreservoir and said sensing provides signal S^(B) _(J+1) (where S^(B) isthe signal from sensor B, j+1 is the time interval), and an analytemeasurement value is obtained using analyte-related signal from sensor Aand sensor B.
 6. The method of claim 5, wherein said rolling analytemeasurement values are calculated as follows: (average signal)_(J)=(S^(B) _(J−1) +S ^(A) _(j))/2,   Eqn. 1 (average signal)_(J+1)=(S ^(A)_(j) +S ^(B) _(j+1))/2;   Eqn. 2 (average signal)_(J+2)=(S ^(B) _(J+1)+S ^(A) _(J+2))/2; etc.,   Eqn. 3 wherein (i) (j−1) is the measurementhalf-cycle previous to j, and (j+2) is two measurement half-cycles afterj, and (ii) each average signal corresponds to a rolling analytemeasurement value.
 7. The method of claim 5, wherein said rollinganalyte measurement values are calculated as follows: (summedsignal)_(J)=(S ^(B) _(j−1) +S ^(A) _(j));   Eqn. 4 (summedsignal)_(j+1)=(S ^(A) _(j) +S ^(B) _(j+1)); and   Eqn. 5 (summedsignal)_(j+2)=(S ^(B) _(j+1) +S ^(A) _(j+2)); etc.   Eqn. 6 where (j−1)is the measurement half-cycle previous to j, and (j+2) is twomeasurement half-cycles after j; and (ii) each summed signal correspondsto a rolling analyte measurement value.
 8. The method of claim 1,wherein a missing or error-associated signal in the series ofanalyte-related signals obtained from the analyte monitoring device isestimated using interpolation before mathematically computing rollinganalyte measurement values.
 9. The method of claim 1, wherein a missingor error-associated signal in the series of analyte-related signalsobtained from the analyte monitoring device is estimated usingextrapolation before mathematically computing rolling analytemeasurement values.
 10. The method of claim 1, wherein said analyte isglucose.
 11. The method of claim 10, wherein said analyte monitoringdevice comprises (i) an iontophoretic sampling device, and (ii) anelectrochemical sensing device.
 12. The method of claim 11, wherein saidanalyte-related signal is a current or a charge related to amount orconcentration of analyte in the subject.
 13. The method of claim 1,wherein one or more microprocessors are utilized to mathematicallycompute rolling analyte measurement values.
 14. The method of claim 13,wherein said one or more microprocessors further control components ofthe analyte monitoring system.
 15. The method of claim 14, wherein saidanalyte monitoring system comprises at least one sampling device and atleast one sensing device.
 16. The method of claim 15, wherein said oneor more microprocessors control obtaining samples from the subject andsensing analyte concentration in each obtained sample to provide theseries of analyte-related signals.
 17. One or more microprocessorsprogrammed to control: mathematical computations of rolling analytemeasurement values, wherein (i) each rolling analyte measurement valueis calculated based on two or more contiguous analyte-related signalsfrom a series of analyte-related signals obtained from an analytemonitoring device, (ii) said series of analyte-related signals isobtained from the analyte monitoring device over time, wherein (a) twoor more contiguous analyte-related signals are used to obtain a singleanalyte measurement value (M), (b) analyte-related signals are not usedto calculate more than one analyte measurement value, and (c) said twoor more contiguous analyte-related signals, used to obtain the singleanalyte measurement value, comprise first and last analyte-relatedsignals of the series, (iii) a subsequent rolling analyte measurementvalue is mathematically computed by dropping said first analyte-relatedsignal and including an analyte-related signal contiguous and subsequentto the last analyte-related signal, (iv) further rolling analytemeasurement values are obtained by repeating the dropping of the firstanalyte-related signal used to calculate the previous rolling analytemeasurement and including an analyte-related signal contiguous andsubsequent to the last analyte-related signal used to calculate theprevious rolling analyte measurement, and (v) each rolling analytemeasurement value provides a measurement related to the amount orconcentration of analyte in the subject; and increases of the number ofanalyte measurement values derived from the analyte-related signals inthe series of analyte-related signals obtained from the analytemonitoring device by serially calculating rolling analyte measurementvalues.
 18. The one or more microprocessors of claim 17, wherein saidanalyte monitoring device comprises at least one sensing device and saidone or more microprocessors are further programmed to control operationof said sensing devices.
 19. The one or more microprocessors of claim18, wherein said analyte monitoring device further comprises at leastone sampling device and said one or more microprocessors are furtherprogrammed to control operation of said sampling devices.
 20. An analytemonitoring device comprising: a sensing device; and one or moremicroprocessor programmed to control operation of said sensing deviceand said one or more microprocessor programmed to control mathematicalcomputations of rolling analyte measurement values, wherein (i) eachrolling analyte measurement value is calculated based on two or morecontiguous analyte-related signals from a series of analyte-relatedsignals obtained from an analyte monitoring device, (ii) said series ofanalyte-related signals is obtained from the analyte monitoring deviceover time, wherein (a) two or more contiguous analyte-related signalsare used to obtain a single analyte measurement value (M), (b)analyte-related signals are not used to calculate more than one analytemeasurement value, and (c) said two or more contiguous analyte-relatedsignals, used to obtain the single analyte measurement value, comprisefirst and last analyte-related signals of the series, (iii) a subsequentrolling analyte measurement value is mathematically computed by droppingsaid first analyte-related signal and including an analyte-relatedsignal contiguous and subsequent to the last analyte-related signal,(iv) further rolling analyte measurement values are obtained byrepeating the dropping of the first analyte-related signal used tocalculate the previous rolling analyte measurement and including ananalyte-related signal contiguous and subsequent to the lastanalyte-related signal used to calculate the previous rolling analytemeasurement, and (v) each rolling analyte measurement value provides ameasurement related to the amount or concentration of analyte in thesubject; and increase of the number of analyte measurement valuesderived from the analyte-related signals in the series ofanalyte-related signals obtained from the analyte monitoring device byserially calculating rolling analyte measurement values.
 21. The analytemonitoring device of claim 20, wherein said analyte monitoring devicefurther comprises a sampling device and said one or more microprocessorsare further programmed to control the operation of said sampling device.22. A method of replacing unusable analyte-related signals whenemploying an analyte monitoring device to measure an analyte amount orconcentration in a subject, said method comprising providing a series ofanalyte-related signals obtained from the analyte monitoring device overtime, wherein each analyte-related signal is related to the amount orconcentration of analyte in the subject; and replacing an unusableanalyte-related signal with an estimated signal by either: (A) if one ormore analyte-related signals previous to the unusable analyte-relatedsignal and one or more analyte-related signals subsequent to theunusable analyte related signal are available, then interpolation isused to estimate the unusable, intervening analyte-related signal; or(B) if two or more analyte-related signals previous to the unusableanalyte-related signal are available, then extrapolation is used toestimate the unusable, subsequent analyte-related signal.
 23. The methodof claim 22, wherein said analyte monitoring device comprises one ormore sensor devices and a relationship between the signals obtained fromthe different sensor devices is used in interpolation and/orextrapolation calculation of estimated values.
 24. The method of claim23, wherein said sensor device comprises two sensor elements and a ratioof signals obtained from a first sensor relative to a second sensor isemployed in interpolation and/or extrapolation calculation of estimatedsignal values.
 25. The method of claim 22, said analyte monitoringdevice comprising a sampling device and a sensing device, said providingthe series of analyte-related signals obtained from an analytemonitoring device comprising extracting a sample from the subjectalternately into a first collection reservoir and then into a secondcollection reservoir using the sampling device, wherein (i) each samplecomprises the analyte, and (ii) said sampling device comprises saidfirst and second collection reservoirs; and sensing the analyte in eachextracted sample to obtain a signal from each sample that is related tothe analyte amount or concentration in the subject, thus providing aseries of analyte-related signals, said sensing device comprising firstand second sensors, wherein said first sensor is in operative contactwith said first collection reservoir and said sensing provides signalS^(A) _(J) (where S^(A) is the signal from sensor A, j is the timeinterval), the second sensor is in operative contact with the secondcollection reservoir and said sensing provides signal S^(B) _(j+1)(where S^(B) is the signal from sensor B, j+1 is the time interval), andan analyte measurement value is obtained using analyte-related signalfrom sensor A and sensor B.
 26. The method of claim 25, wherein saidanalyte monitoring device comprises two sensors and a relationshipbetween the signals obtained from the different sensors is used ininterpolation and/or extrapolation calculation of estimated values. 27.The method of claim 26, wherein said relationship between the signalsfrom the different sensors is a smoothed ratio of the form: R ^(S) ₁ =wR₁+(1-−w)R ^(S) _(l−1)   Eqn. 10 wherein, R₁ is the A/B or B/A signalratio for a i^(th) measurement cycle, R^(S) _(i) is smoothed R for ai^(th) measurement cycle, and w is a smoothing factor and is representedby a fraction between and inclusive of 0 through 1, and R^(S) _(i−1) isa smoothed ratio for the (i−1)^(th) measurement cycle, wherein thei^(th) measurement cycle is composed of first and second half-cycles andthe second half-cycle value of the i^(th) measurement cycle precedesS_(j).
 28. The method of claim 27, wherein a smoothed A/B ratio and asmoothed B/A ratio are employed, and said ratios are as follows:$\begin{matrix}{\left( \frac{A}{B} \right)_{s,i} = {{w\left( \frac{A}{B} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{A}{B} \right)_{s,{i - 1}}}}} & {{Eqn}.\quad \text{9A}} \\{\left( \frac{B}{A} \right)_{s,i} = {{w\left( \frac{B}{A} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{B}{A} \right)_{s,{i - 1}}}}} & {{Eqn}.\quad \text{9B}}\end{matrix}$

wherein (A/B)_(s,i) and (B/A)_(s,i) refer to “smoothed” AB ratios formeasurement cycle i, (A/B)_(i) and (B/A)_(i), refer to the AB ratio formeasurement cycle i, and (A/B)_(s,i−1) and (B/A)_(s,i−1), refer to thesmoothed AB ratio from the previous measurement cycle i-1.
 29. Themethod of claim 28, wherein said analyte is glucose.
 30. The method ofclaim 28, for interpolation in the situation where both S_(j) andS_(J+2) are signals from the B sensor (S^(B) _(j) and S^(B) _(J+2)), andS_(J+1) is being estimated for the A sensor signal (S^(AE) _(j+1)),interpolation Eqn. 7A is employed as follows: $\begin{matrix}{S_{j + 1}^{AE} = {\frac{A}{B}\left\{ {S_{j}^{B} + {\left( {S_{j + 2}^{B} - S_{j}^{B}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\quad \text{7A}}\end{matrix}$

wherein t_(j) is a measurement half-cycle, t_(J+1), one subsequenthalf-cycle, and t_(J+2) two subsequent half-cycles.
 31. The method ofclaim 28, for interpolation in the situation where both S_(J) andS_(J+2) are signals from the A sensor (S^(A) _(J) and S^(A) _(j+2)), andS_(J+1) is being estimated for the B sensor signal (S^(BE) _(J+1)),interpolation Eqn. 7C is employed as follows: $\begin{matrix}{S_{j + 1}^{BE} = {\frac{B}{A}\left\{ {S_{j}^{A} + {\left( {S_{j + 2}^{A} - S_{j}^{A}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\quad \text{7C}}\end{matrix}$

wherein t_(j) is a measurement half-cycle, t_(J+1), one subsequenthalf-cycle, and t_(J+2) two subsequent half-cycles.
 32. The method ofclaim 28, for extrapolation in the situation where S_(J) is signal fromsensor A (S^(A) _(J)) and S_(J+1) is signal from B sensor (S^(B)_(J+1)), and S_(j+2) is being estimated for the A sensor signal (S^(AE)_(j+2)), extrapolation Eqn. 8A is employed as follows: $\begin{matrix}{S_{j + 2}^{AE} = {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} + \left\lbrack {\left\{ {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} - S_{j}^{A}} \right\} \frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\quad \text{8A}}\end{matrix}$

wherein t_(J) is a measurement half-cycle, t_(J+1), one subsequenthalf-cycle, and t_(j+2) two subsequent half-cycles.
 33. The method ofclaim 28, for extrapolation in the situation where S_(J) is signal fromthe B sensor (S^(B) _(J)) and S_(j+1) is signal from the A sensor (S^(A)_(j+1)), and S_(j+2) is being estimated for the B sensor signal (S^(BE)_(j+2)), extrapolation Eqn. 8C is employed as follows: $\begin{matrix}{S_{j + 2}^{BE} = {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} + \left\lbrack {\left\{ {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} - S_{j}^{B}} \right\} \frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\quad \text{8C}}\end{matrix}$

wherein t_(j) is a measurement half-cycle, t_(J+1), one subsequenthalf-cycle, and t_(j+2) two subsequent half-cycles.
 34. The method ofclaim 22, wherein said analyte is glucose.
 35. The method of claim 34,wherein said analyte monitoring device comprises (i) an iontophoreticsampling device, and (ii) an electrochemical sensing device.
 36. Themethod of claim 35, wherein said analyte-related signal is a current ora charge related to analyte amount or concentration of analyte in thesubject.
 37. The method of claim 22, wherein one or more microprocessorsare utilized to mathematically compute said estimated signal.
 38. Themethod of claim 37, wherein said one or more microprocessors furthercontrol components of the analyte monitoring system.
 39. The method ofclaim 38, wherein said analyte monitoring system comprises a samplingdevice and a sensing device.
 40. The method of claim 39, wherein saidone or more microprocessors control obtaining samples from the subjectand sensing analyte concentration in each obtained sample to provide theseries of analyte-related signals.
 41. One or more microprocessorsprogrammed to control: replacement, in a series of analyte-relatedsignals, of an unusable analyte-related signal with an estimated signalby either: (A) if one or more analyte-related signals previous to theunusable analyte-related signal and one or more analyte-related signalssubsequent to the unusable analyte related signal are available, theninterpolation is used to estimate the unusable, interveninganalyte-related signal; or (B) if two or more analyte-related signalsprevious to the unusable analyte-related signal are available, thenextrapolation is used to estimate the unusable, subsequentanalyte-related signal; wherein said series of analyte-related signalsis obtained from an analyte monitoring device over time, and eachanalyte-related signal is related to an amount or concentration ofanalyte in a subject being monitored with the analyte monitoring device.42. The one or more microprocessors of claim 41, wherein said analytemonitoring device comprises a sensing device and said one or moremicroprocessors are further programmed to control operation of saidsensing device.
 43. The one or more microprocessors of claim 42, whereinsaid analyte monitoring device further comprises a sampling device andsaid one or more microprocessors are further programmed to controloperation of said sampling device.
 44. An analyte monitoring devicecomprising: a sensing device; and one or more microprocessor programmedto control operation of said sensing device and said one or moremicroprocessor programmed to control replacement, in a series ofanalyte-related signals, of an unusable analyte-related signal with anestimated signal by either: (A) if one or more analyte-related signalsprevious to the unusable analyte-related signal and one or moreanalyte-related signals subsequent to the unusable analyte relatedsignal are available, then interpolation is used to estimate theunusable, intervening analyte-related signal; or (B) if two or moreanalyte-related signals previous to the unusable analyte-related signalare available, then extrapolation is used to estimate the unusable,subsequent analyte-related signal; wherein said series ofanalyte-related signals is obtained from an analyte monitoring deviceover time, and each analyte-related signal is related to an amount orconcentration of analyte in a subject being monitored with the analytemonitoring device.
 45. The analyte monitoring device of claim 44,wherein said analyte monitoring device further comprises a samplingdevice and said one or more microprocessors are further programmed tocontrol the operation of said sampling device.
 46. A method for reducingthe incidence of failed calibration for an analyte monitoring systemthat is used to monitor an amount or concentration of analyte present ina subject, wherein the analyte monitoring system provides a series ofsignals or measurement values, said method comprising: sensing theanalyte in each of a series of samples to obtain an analyte-relatedsignal from each sample that is related to the analyte amount orconcentration in the subject, thus providing a series of analyte-relatedsignals, said sensing device comprising a first sensor (A) and secondsensor (B), wherein (1) said first sensor (A) is in operative contactwith a first collection reservoir and said second sensor (B) is inoperative contact with a second collection reservoir, and (2) twoconsecutive analyte-related signals comprise a measurement cycle, andeach of the two consecutive analyte-related signals is half-cycleanalyte-related signal; and performing a calibration method to relateanalyte amount or concentration in the subject to analyte-relatedsignals obtained from the sensors, said calibration method comprising:(i) obtaining a first half-cycle analyte-related signal S_(j), wherein asecond half-cycle analyte-related signal S_(j+1), or an estimatethereof, and a third half-cycle analyte-related signal S_(j+2), or anestimate thereof, are both used in the calibration method so that thesensor analyte-related signals correlate to the analyte amount orconcentration in the subject, wherein the calibration method alsoemploys an analyte calibration value that is independently determined;(ii) providing the analyte calibration value; (iii) selecting aconditional statement selected from the group consisting of: (a) ifneither the second half-cycle signal S_(J+1) nor the third half-cyclesignal S_(j+2) comprise errors, then S_(j+1) and S_(j+2) are used in thecalibration method; (b) if only the second half-cycle signal S_(j+1)comprises an error, then an estimated signal S^(E) _(J+1) is obtained bydetermining an interpolated value using signal S_(J) and S_(j+2),wherein said interpolated value is S^(E) _(J+1), and S^(E) _(J+1) andS_(J+2) are used in the calibration method; (c) if only the thirdhalf-cycle signal S_(j+2) comprises an error, then an estimated signalS^(E) _(J+2) is obtained by determining an extrapolated value usingsignal S_(J) and S_(J+1), wherein said extrapolated value is S^(E)_(j+2), and S_(j+1) and S^(E) _(j+2) are used in the calibration method;and (d) if both the second half-cycle signal S_(J+1) and the thirdhalf-cycle signal S_(J+2) comprise errors, then return to (i) to obtaina new half-cycle signal S_(j) from a later measurement half-cycle thanthe first half-cycle signal, wherein said calibration method reduces theincidence of failed calibration for the analyte monitoring system. 47.The method of claim 46, wherein said analyte monitoring device comprisestwo sensors and a relationship between the signals obtained from thedifferent sensors is used in interpolation and/or extrapolationcalculation of estimated values.
 48. The method of claim 47, whereinsaid relationship between the signals from the different sensors is asmoothed ratio of the form: R ^(S) _(i) =wR _(i)+(1−w)R ^(s) _(l−1)  Eqn. 10 wherein, R_(i) is the A/B or B/A signal ratio for a i^(th)measurement cycle, R^(S) ₁ is smoothed R for a i^(th) measurement cycle,and w is a smoothing factor and is represented by a fraction between andinclusive of 0 through 1, and R^(S) _(i−1) is a smoothed ratio for the(i−1)^(th) measurement cycle, wherein the i^(th) measurement cycle iscomposed of first and second half-cycles and the second half-cycle valueof the i^(th) measurement cycle precedes S_(J).
 49. The method of claim48, wherein a smoothed A/B ratio and a smoothed B/A ratio are employed,and said ratios are as follows: $\begin{matrix}{\left( \frac{A}{B} \right)_{s,i} = {{w\left( \frac{A}{B} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{A}{B} \right)_{s,{i - 1}}}}} & {{Eqn}.\quad \text{9A}} \\{\left( \frac{B}{A} \right)_{s,i} = {{w\left( \frac{B}{A} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{B}{A} \right)_{s,{i - 1}}}}} & {{Eqn}.\quad \text{9B}}\end{matrix}$

wherein (A/B)_(s,1) and (B/A)_(s,1) refer to “smoothed” AB ratios formeasurement cycle i, (A/B)_(i) and (B/A)_(i), refer to the AB ratio formeasurement cycle i, and (A/B)_(s,1−1) and (B/A)_(s,i−1), refer to thesmoothed AB ratio from the previous measurement cycle i−1.
 50. Themethod of claim 49, wherein said analyte is glucose.
 51. The method ofclaim 49, for interpolation in the situation where both S_(J) andS_(j+2) are signals from the B sensor (S^(B) _(j) and S^(B) _(j+2)), andS_(j+1) is being estimated for the A sensor signal (S^(AE) _(j+1)),interpolation Eqn. 7A is employed as follows: $\begin{matrix}{S_{j + 1}^{AE} = {\frac{A}{B}\left\{ {S_{j}^{B} + {\left( {S_{j + 2}^{B} - S_{j}^{B}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\quad \text{7A}}\end{matrix}$

wherein t_(J) is a measurement half-cycle, t_(J+1), one subsequenthalf-cycle, and t_(j+2) two subsequent half-cycles.
 52. The method ofclaim 49, for interpolation in the situation where both S_(j) andS_(J+2) are signals from the A sensor (S^(A) _(J) and S^(A) _(J+2)), andS_(J+1) is being estimated for the B sensor signal (S^(BE) _(j+1)),interpolation Eqn. 7C is employed as follows: $\begin{matrix}{S_{j + 1}^{BE} = {\frac{B}{A}\left\{ {S_{j}^{A} + {\left( {S_{j + 2}^{A} - S_{j}^{A}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\quad \text{7C}}\end{matrix}$

wherein t_(j) is a measurement half-cycle, t_(j+1), one subsequenthalf-cycle, and t_(J+2) two subsequent half-cycles.
 53. The method ofclaim 49, for extrapolation in the situation where S_(J) is signal fromsensor A (S^(A) _(J)) and S_(j+1) is signal from B sensor (S^(B)_(j+1)), and S_(J+2) is being estimated for the A sensor signal (S^(AE)_(J+2)), extrapolation Eqn. 8A is employed as follows: $\begin{matrix}{S_{j + 2}^{AE} = {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} + \left\lbrack {\left\{ {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} - S_{j}^{A}} \right\} \frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\quad \text{8A}}\end{matrix}$

wherein t_(J) is a measurement half-cycle, t_(j+1), one subsequenthalf-cycle, and t_(j+2) two subsequent half-cycles.
 54. The method ofclaim 49, for extrapolation in the situation where S_(j) is signal fromthe B sensor (S^(B) _(j)) and S_(j+1) is signal from the A sensor (S^(A)_(j+1)), and S_(j+2) is being estimated for the B sensor signal (S^(BE)_(J+2)), extrapolation Eqn. 8C is employed as follows: $\begin{matrix}{S_{j + 2}^{BE} = {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} + \left\lbrack {\left\{ {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} - S_{j}^{B}} \right\} \frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\quad \text{8C}}\end{matrix}$

wherein t_(j) is a measurement half-cycle, t_(j+1), one subsequenthalf-cycle, and t_(J+2) two subsequent half-cycles.
 55. The method ofclaim 46, wherein said analyte is glucose.
 56. The method of claim 55,wherein said analyte monitoring device comprises (i) an iontophoreticsampling device, and (ii) an electrochemical sensing device.
 57. Themethod of claim 56, wherein said analyte-related signal is a current ora charge related to analyte amount or concentration of analyte in thesubject.
 58. The method of claim 46, wherein one or more microprocessorsare utilized to perform said calibration method.
 59. The method of claim58, wherein said one or more microprocessors further control componentsof the analyte monitoring system.
 60. The method of claim 59, whereinsaid analyte monitoring system comprises a sampling device and a sensingdevice.
 61. The method of claim 60, wherein said one or moremicroprocessors control obtaining samples from the subject and sensinganalyte concentration in each obtained sample to provide the series ofanalyte-related signals.
 62. The method of claim 46, further comprisingwaiting for an un-skipped half-cycle signal (S_(j)) before initiatingsaid calibration method.
 63. One or more microprocessors programmed tocontrol: operation of a sensing device for sensing an analyte in each ofa series of samples to obtain an analyte-related signal from each samplethat is related to analyte amount or concentration in a subject, thusproviding a series of analyte-related signals, said sensing devicecomprising a first sensor (A) and second sensor (B), wherein (1) saidfirst sensor (A) is in operative contact with a first collectionreservoir and said second sensor (B) is in operative contact with asecond collection reservoir, and (2) two consecutive analyte-relatedsignals comprise a measurement cycle, and each of the two consecutiveanalyte-related signals is half-cycle analyte-related signal; andperformance of a calibration method to relate analyte amount orconcentration in the subject to analyte-related signals obtained fromthe sensors, said calibration method comprising: (i) obtaining a firsthalf-cycle analyte-related signal S_(j), wherein a second half-cycleanalyte-related signal S_(J+1), or an estimate thereof, and a thirdhalf-cycle analyte-related signal S_(J+2), or an estimate thereof, areboth used in the calibration method so that the sensor analyte-relatedsignals correlate to the analyte amount or concentration in the subject,wherein the calibration method also employs an analyte calibration valuethat is independently determined; (ii) providing the analyte calibrationvalue; (iii) selecting a conditional statement selected from the groupconsisting of: (a) if neither the second half-cycle signal S_(j+1) northe third half-cycle signal S_(J+2) comprise errors, then S_(J+1) andS_(J+2) are used in the calibration method; (b) if only the secondhalf-cycle signal S_(J+1) comprises an error, then an estimated signalS^(E) _(J+1) is obtained by determining an interpolated value usingsignal S_(j) and S_(j+2), wherein said interpolated value is S^(E)_(J+1), and S^(E) _(j+1) and S_(j+2) are used in the calibration method;(c) if only the third half-cycle signal S_(j+2) comprises an error, thenan estimated signal S^(E) _(J+2) is obtained by determining anextrapolated value using signal S_(j) and S_(j+1), wherein saidextrapolated value is S^(E) _(J+2), and S_(j+1) and S^(E) _(j+2) areused in the calibration method; and (d) if both the second half-cyclesignal S_(j+1) and the third half-cycle signal S_(J+2) comprise errors,then return to (i) to obtain a new half-cycle signal S_(J) from a latermeasurement half-cycle than the first half-cycle signal.
 64. The one ormore microprocessors of claim 63, wherein said analyte monitoring devicecomprises a sensing device and said one or more microprocessors arefurther programmed to control operation of said sensing device.
 65. Theone or more microprocessors of claim 64, wherein said analyte monitoringdevice further comprises a sampling device and said one or moremicroprocessors are further programmed to control operation of saidsampling device.
 66. An analyte monitoring device comprising: a sensingdevice; and one or more microprocessor programmed to control operationof said sensing device and said one or more microprocessor programmed tocontrol performance of a calibration method to relate analyte amount orconcentration in the subject to analyte-related signals obtained fromthe sensors, said calibration method comprising: (i) obtaining a firsthalf-cycle analyte-related signal S,, wherein a second half-cycleanalyte-related signal S_(J+1), or an estimate thereof, and a thirdhalf-cycle analyte-related signal S_(j+2), or an estimate thereof, areboth used in the calibration method so that the sensor analyte-relatedsignals correlate to the analyte amount or concentration in the subject,wherein the calibration method also employs an analyte calibration valuethat is independently determined; (ii) providing the analyte calibrationvalue; (iii) selecting a conditional statement selected from the groupconsisting of: (a) if neither the second half-cycle signal S_(J+1) northe third half-cycle signal S_(j+2) comprise errors, then S_(J+1) andS_(J+2) are used in the calibration method; (b) if only the secondhalf-cycle signal S_(j+1) comprises an error, then an estimated signalS^(E) _(j+1) is obtained by determining an interpolated value usingsignal S_(J) and S_(J+2), wherein said interpolated value is S^(E)_(J+1), and S^(E) _(J+1) and S_(j+2) are used in the calibration method;(c) if only the third half-cycle signal S_(j+2) comprises an error, thenan estimated signal S^(E) _(j+2) is obtained by determining anextrapolated value using signal S_(J) and S_(J+1), wherein saidextrapolated value is S^(E) _(J+2), and S_(j+1) and S^(E) _(J+2) areused in the calibration method; and (d) if both the second half-cyclesignal S_(J+1) and the third half-cycle signal S_(J+2) comprise errors,then return to (i) to obtain a new half-cycle signal S_(j) from a latermeasurement half-cycle than the first half-cycle signal.
 67. The analytemonitoring device of claim 66, wherein said analyte monitoring devicefurther comprises a sampling device and said one or more microprocessorsare further programmed to control the operation of said sampling device.68. A method for predicting an analyte concentration-related event in asubject being monitored for levels of a selected analyte using ananalyte monitoring device, said method comprising providing (A) a seriesof analyte-related signals obtained from the analyte monitoring deviceover time, wherein each analyte-related signal is related to the amountor concentration of analyte in the subject, (B) a predeterminedthreshold value indicative of an analyte concentration-related event inthe subject, and (C) a predetermined threshold rate of change of analyteconcentration indicative of an analyte concentration-related event inthe subject; calculating (A) a predicted analyte-related signal at afuture time interval, which occurs after the series of analyte-relatedsignals is obtained, using one or more predictive algorithms, and (B) arate of change of analyte concentration in the subject; comparing (A)the predicted analyte-related signal to the threshold value, then (i) ifthe predicted value is within the threshold value, then the analytemonitoring device continues to provide a series of analyte-relatedsignals and repeats said calculating and comparing of a predictedanalyte-related signal based on the series of analyte-related signalscomprising new analyte-related signal obtained subsequent to theprevious predicted analyte-related signal, or (ii) if the predictedvalue is beyond the threshold value, then an analyteconcentration-related event is indicated, and the analyte monitoringdevice continues to provide a series of analyte-related signals andrepeats said calculating and comparing of a predicted analyte-relatedsignal based on the series of analyte-related signals comprising newanalyte-related signal obtained subsequent to the previous predictedanalyte-related signal; (B) the rate of change of analyte concentrationin the subject to the predetermined threshold rate of change, then (i)if the rate of change is beyond the predetermined threshold rate ofchange then an analyte concentration-related event is indicated and theanalyte monitoring system continues to provide a series ofanalyte-related signals and repeats said calculating of a rate of changebased on the series of analyte-related signals comprising newanalyte-related signal obtained subsequent to the previous predictedanalyte-related signal, or (ii) if the rate of change is within thepredetermined threshold rate of change, then the analyte monitoringsystem continues to provide a series of analyte-related signals andrepeats said calculating of a rate of change based on the series ofanalyte-related signals comprising new analyte-related signal obtainedsubsequent to the previous predicted analyte-related signal; andpredicting an analyte concentration-related event in a subject if boththe predicted value indicates an analyte concentration-related event andthe rate of change indicates an analyte concentration-related event. 69.The method of claim 68, wherein when an analyte concentration-relatedevent is predicted an alert is provided to the subject to indicate ananalyte-concentration related event.
 70. The method of claim 68, whereinsaid one or more predictive algorithms are selected from the groupconsisting of: $\begin{matrix}{y_{({n + 1})} = {y_{(n)} + {\alpha \left( {y_{(n)} - y_{({n - 1})}} \right)} + {\frac{\alpha^{2}}{2}\left( {y_{(n)} - {2y_{({n - 1})}} + y_{({n - 2})}} \right)}}} & {{Eqn}.\quad 11} \\{y_{({n + 1})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 1})} - t_{n}} \right)}}} & {{Eqn}.\quad 12} \\{y_{({n + 1})} = {{\frac{5}{2}y_{(n)}} + {{- 2}\left( y_{({n - 1})} \right)} + {\frac{1}{2}\left( y_{({n - 2})} \right)}}} & {{Eqn}.\quad 13} \\{y_{({n + 2})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 2})}} \right)}{\left( {t_{n} - t_{({n - 2})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} & {{Eqn}.\quad 14} \\{y_{({n + 2})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} & {{Eqn}.\quad 15}\end{matrix}$

wherein y_((n+1)) and y_((n+2)) are predicted values of variable y attime points (n+1) and (n+2), respectively, y_((n)), Y_((n−1)), Y_((n−2))are analyte-related values at times (n), (n−1), and (n−2), respectively,t_((n−2)), t_((n−1)), t_((n)), t_((n+1)), t_((n+2)), are time points attimes (n−2), (n−1), (n), (n+1) and (n+2), respectively, and a is anempirically determined weighting value that is a real number between 0and
 1. 71. The method of claim 68, wherein said rate of change ofanalyte concentration in the subject is determined using one or moregradient models selected from the group consisting of: $\begin{matrix}{{{{Model}\quad A\text{:}\quad \frac{y_{(n)} - y_{({n - 1})}}{\Delta \quad t}\left( {{concentration}/{time}} \right)};\quad {{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 1})}} \right)}}\quad} \\{{{Model}\quad B\text{:}\quad \frac{y_{(n)} - y_{({n - 1})}}{y_{({n - 1})}\Delta \quad t}\left( {{fractional}\quad {{change}/{time}}} \right)};\quad {{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 1})}} \right)}}\end{matrix}$${{{Model}\quad C\text{:}\quad \frac{y_{(n)} - y_{({n - 2})}}{\Delta \quad t}\left( {{concentration}/{time}} \right)};\quad {{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 2})}} \right)}}\quad$${{{Model}\quad D\text{:}\quad \frac{y_{(n)} - y_{({n - 2})}}{y_{({n - 2})}\Delta \quad t}\left( {{fractional}\quad {{change}/{time}}} \right)};\quad {{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 2})}} \right)}}\quad$${{Model}\quad E\text{:}\quad {{Average}\left\lbrack {\frac{y_{(n)} - y_{({n - 1})}}{\Delta \quad t_{1}},\frac{y_{({n - 1})} - y_{({n - 2})}}{\Delta \quad t_{2}},\frac{y_{(n)} - y_{({n - 2})}}{\Delta \quad t_{3}}} \right\rbrack}\quad \left( {{concentration}/{time}} \right)};$

where Δt₁=(t_((n))−t_((n−1))), Δt₂=(t_((n−1))−t_((n−2))), andΔt₃=(t_((n))-t_((n−2))), and the average is of all three values shown inbrackets; and${{{Model}\quad F\text{:}\quad \frac{y_{(n)} - y_{({n - 3})}}{y_{({n - 3})}\Delta \quad t}\quad \left( {{fractional}\quad {{change}/{time}}} \right)};\quad {{{where}\quad \Delta \quad t} = \left( {t_{(n)} - t_{({n - 3})}} \right)}},{wherein}$

these models, y_(n) stands for an analyte reading at time point t_((n)),y_((n−1)) an analyte reading at time point t_((n−1)), y_((n−2)) ananalyte reading at time point t_((n−2)), y_((n−3)) an analyte reading attime point t_((n−3)).
 72. The method of claim 68, wherein saidpredictive algorithm is $\begin{matrix}{y_{({n + 2})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} & {{Eqn}.\quad 15}\end{matrix}$

wherein y_((n+2)) is a predicted value of variable y at time point(n+2), y_((n)), and y_((n−1)) are analyte-related values at times (n),and (n−1), respectively, t_((n−1)), t_((n)), and t_((n+2)), are timepoints at times (n−1), (n), and (n+2), respectively; and said rate ofchange of analyte concentration in the subject is determined using${{Model}\quad B\text{:}\quad \frac{y_{(n)} - y_{({n - 1})}}{y_{({n - 1})}\Delta \quad t}\quad \left( {{fractional}\quad {{change}/{time}}} \right)};{{{where}\quad \Delta \quad t} = {\left( {t_{(n)} - t_{({n - 1})}} \right).}}$


73. The method of claim 68, wherein said analyte is glucose.
 74. Themethod of claim 73, wherein (i) said predicted glucose-related signal,at a further time interval, is lower than the predetermined thresholdsaid predicted glucose-related signal is designated to be hypoglycemic,and (ii) said predicted rate of change is negative and less than apredetermined threshold rate of change is indicative of a hypoglycemicevent; and (iii) a hypoglycemic event in the subject is predicted whenboth (a) comparing the predicted measurement value to the predeterminedthreshold indicates a hypoglycemic event, and (b) comparing the rate ofchange with the threshold rate of change indicates a hypoglycemic event.75. The method of claim 73, wherein said predictive algorithm is$\begin{matrix}{{GW}_{({n + 2})} = {{GW}_{(n)} + {\frac{\left( {{GW}_{(n)} - {GW}_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*{\left( {t_{({n + 2})} - t_{n}} \right).}}}} & {{Eqn}.\quad 16}\end{matrix}$

wherein GW_((n+2)) is a predicted glucose value of variable GW at timepoint (n+2), GW_((n)), and GW_((n−1)) are analyte-related values attimes (n), and (n−1), respectively, t_((n−1)), t_((n)), and t_((n+2)),are time points at times (n−1), (n), and (n+2), respectively; and saidrate of change of analyte concentration in the subject is determinedusing${{Model}\quad B^{\prime}\quad \frac{{GW}_{(n)} - {GW}_{({n - 1})}}{{GW}_{({n - 1})}\Delta \quad t}},{{{where}\quad \Delta \quad t} = {\left( {t_{(n)} - t_{({n - 1})}} \right).}}$


76. The method of claim 73, wherein said analyte-concentration relatedevent in the subject is hypoglycemia.
 77. The method of claim 73,wherein said analyte-concentration related event in the subject ishyperglycemia.
 78. The method of claim 73, wherein said analytemonitoring device comprises (i) an iontophoretic sampling device, and(ii) an electrochemical sensing device.
 79. The method of claim 78,wherein said analyte-related signal is a current or a charge related toanalyte amount or concentration of glucose in the subject.
 80. Themethod of claim 68, wherein one or more microprocessors are utilized toperform said calculating and comparing.
 81. The method of claim 80,wherein when an analyte concentration-related event is predicted saidone or more microprocessors triggers the analyte monitoring system toprovide an alert to the subject to indicate an analyte-concentrationrelated event.
 82. The method of claim 80, wherein said one or moremicroprocessors further control components of the analyte monitoringsystem.
 83. The method of claim 82, wherein said analyte monitoringsystem comprises a sampling device and a sensing device.
 84. The methodof claim 83, wherein said one or more microprocessors control obtainingsamples from the subject and sensing analyte concentration in eachobtained sample to provide the series of analyte-related signals. 85.The method of claim 76, wherein further parameters are used to confirm aprediction of the hypoglycemic event, such further parameters selectedfrom the group consisting of current glucose readings in the subject,one or more predicted glucose reading, time intervals, trends, skinconductance, and skin temperature.
 86. One or more microprocessorsprogrammed to control: provision of (A) a series of analyte-relatedsignals obtained from the analyte monitoring device over time, whereineach analyte-related signal is related to the amount or concentration ofanalyte in the subject, (B) a predetermined threshold value indicativeof an analyte concentration-related event in the subject, and (C) apredetermined threshold rate of change of analyte concentrationindicative of an analyte concentration-related event in the subject;calculation of (A) a predicted analyte-related signal at a future timeinterval, which occurs after the series of analyte-related signals isobtained, using one or more predictive algorithms, wherein and (B) arate of change of analyte concentration in a subject; comparison of (A)the predicted analyte-related signal to the threshold value, then (i) ifthe predicted value is within the threshold value, then the analytemonitoring device continues to provide a series of analyte-relatedsignals and repeats said calculating and comparing of a predictedanalyte-related signal based on the series of analyte-related signalscomprising new analyte-related signal obtained subsequent to theprevious predicted analyte-related signal, or (ii) if the predictedvalue is beyond the threshold value, then an analyteconcentration-related event is indicated, and the analyte monitoringdevice continues to provide a series of analyte-related signals andrepeats said calculating and comparing of a predicted analyte-relatedsignal based on the series of analyte-related signals comprising newanalyte-related signal obtained subsequent to the previous predictedanalyte-related signal; (B) the rate of change of analyte concentrationin the subject to the predetermined threshold rate of change, then (i)if the rate of change is beyond the predetermined threshold rate ofchange then an analyte concentration-related event is indicated and theanalyte monitoring system continues to provide a series ofanalyte-related signals and repeats said calculating of a rate of changebased on the series of analyte-related signals comprising newanalyte-related signal obtained subsequent to the previous predictedanalyte-related signal, or (ii) if the rate of change is within thepredetermined threshold rate of change, then the analyte monitoringsystem continues to provide a series of analyte-related signals andrepeats said calculating of a rate of change based on the series ofanalyte-related signals comprising new analyte-related signal obtainedsubsequent to the previous predicted analyte-related signal; andprediction of an analyte concentration-related event in a subject ifboth the predicted value indicates an analyte concentration-relatedevent and the rate of change indicates an analyte concentration-relatedevent.
 87. The one or more microprocessors of claim 86, wherein saidanalyte monitoring device comprises a sensing device and said one ormore microprocessors are further programmed to control operation of saidsensing device.
 88. The one or more microprocessors of claim 87, whereinsaid analyte monitoring device further comprises a sampling device andsaid one or more microprocessors are further programmed to controloperation of said sampling device.
 89. An analyte monitoring devicecomprising: a sensing device; and one or more microprocessor programmedto control operation of said sensing device and said one or moremicroprocessor programmed to control provision of (A) a series ofanalyte-related signals obtained from the analyte monitoring device overtime, wherein each analyte-related signal is related to the amount orconcentration of analyte in the subject, (B) a predetermined thresholdvalue indicative of an analyte concentration-related event in thesubject, and (C) a predetermined threshold rate of change of analyteconcentration indicative of an analyte concentration-related event inthe subject; calculation of (A) a predicted analyte-related signal at afuture time interval, which occurs after the series of analyte-relatedsignals is obtained, using one or more predictive algorithms, whereinand (B) a rate of change of analyte concentration in a subject;comparison of (A) the predicted analyte-related signal to the thresholdvalue, then (i) if the predicted value is within the threshold value,then the analyte monitoring device continues to provide a series ofanalyte-related signals and repeats said calculating and comparing of apredicted analyte-related signal based on the series of analyte-relatedsignals comprising new analyte-related signal obtained subsequent to theprevious predicted analyte-related signal, or (ii) if the predictedvalue is beyond the threshold value, then an analyteconcentration-related event is indicated, and the analyte monitoringdevice continues to provide a series of analyte-related signals andrepeats said calculating and comparing of a predicted analyte-relatedsignal based on the series of analyte-related signals comprising newanalyte-related signal obtained subsequent to the previous predictedanalyte-related signal; (B) the rate of change of analyte concentrationin the subject to the predetermined threshold rate of change, then (i)if the rate of change is beyond the predetermined threshold rate ofchange then an analyte concentration-related event is indicated and theanalyte monitoring system continues to provide a series ofanalyte-related signals and repeats said calculating of a rate of changebased on the series of analyte-related signals comprising newanalyte-related signal obtained subsequent to the previous predictedanalyte-related signal, or (ii) if the rate of change is within thepredetermined threshold rate of change, then the analyte monitoringsystem continues to provide a series of analyte-related signals andrepeats said calculating of a rate of change based on the series ofanalyte-related signals comprising new analyte-related signal obtainedsubsequent to the previous predicted analyte-related signal; andprediction of an analyte concentration-related event in a subject ifboth the predicted value indicates an analyte concentration-relatedevent and the rate of change indicates an analyte concentration-relatedevent.
 90. The analyte monitoring device of claim 89, wherein saidanalyte monitoring device further comprises a sampling device and saidone or more microprocessors are further programmed to control theoperation of said sampling device.